1 Raw data

data <- read.csv("/home/bambrozi/2_CORTISOL/Data/T4_Elora_Data_04_25_2024.csv")

# Replace "treated" with NA
data$T4Cortisol[data$T4Cortisol == "treated" | data$T4Cortisol == "Treated at T2" | data$T4Cortisol == "treated at T2"] <- NA
# Convert the column to numeric, coercing non-numeric values to NA
data$T4Cortisol <- as.numeric(as.character(data$T4Cortisol))
#Filtering only the lines with values
data <- data[!is.na(data$T4Cortisol),]
#creating new data file cleaned  
write.csv(data, "/home/bambrozi/2_CORTISOL/Data/data_clean.csv", row.names = F)

print(data)

2 Continuous Phenotype

# Summary Statistics
summary(data$T4Cortisol)
# Histogram
hist(data$T4Cortisol, breaks = 20, main = "Histogram of T4 Cortisol", xlab = "T4 Cortisol")
# Boxplot
boxplot(data$T4Cortisol, main = "Boxplot of T4 Cortisol", ylab = "T4 Cortisol")
# Density Plot
plot(density(data$T4Cortisol), main = "Density Plot of T4 Cortisol", xlab = "T4 Cortisol", ylab = "Density")
# Calculate the theoretical quantiles
qqnorm(data$T4Cortisol, main = "QQ Plot of T4Cortisol", xlim = c(min(qqnorm(data$T4Cortisol)$x), max(qqnorm(data$T4Cortisol)$x)), ylim = c(min(qqnorm(data$T4Cortisol)$y), max(qqnorm(data$T4Cortisol)$y) + 2 * IQR(qqnorm(data$T4Cortisol)$y)))
# Add the QQ line
qqline(data$T4Cortisol, col = "red")

Summary statistics My Image

Histogram My Image

Density My Image

Box_Plot My Image

qq_Plot My Image

Shapiro-Wilk normality test My Image

2.1 Categorical Phenotype

I received from Umesh a e-mail informing the three categories that the animals could be sorted based on their cortisol concentration.

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data$Categorical <- ifelse(data$T4Cortisol >= 956, "H", 
                           ifelse(data$T4Cortisol <= 190.8, "L", "M"))

table(data$Categorical)
library(ggplot2)

# Reorder the levels of the 'Categorical' column
data$Categorical <- factor(data$Categorical, levels = c("L", "M", "H"))

# Create the histogram with reordered categories
ggplot(data, aes(x = Categorical, fill = Categorical)) +
  geom_bar() +
  labs(title = "Histogram of T4Cortisol by Category",
       x = "Category",
       y = "Frequency") +
  theme_minimal()

# Create the histogram
ggplot(data, aes(x = T4Cortisol, fill = Categorical)) +
  geom_histogram(binwidth = 50, color = "black", alpha = 0.7) + # Adjust binwidth as needed
  labs(title = "Histogram of T4Cortisol with Color by Category",
       x = "T4 Cortisol",
       y = "Frequency",
       fill = "Category") +
  scale_fill_manual(values = c("H" = "red", "M" = "blue", "L" = "green")) + # Adjust colors if needed
  theme_minimal()

# Create the density plot
ggplot(data, aes(x = T4Cortisol, fill = Categorical)) +
  geom_density(alpha = 0.3) +
  labs(title = "Density Plot of T4Cortisol with Color by Category",
       x = "T4Cortisol",
       y = "Density",
       fill = "Category") +
  scale_fill_manual(values = c("H" = "red", "M" = "blue", "L" = "green")) + # Adjust colors if needed
  theme_minimal()

# Create a density plot
ggplot(data, aes(x = T4Cortisol)) +
  geom_density() +
  geom_vline(xintercept = c(956, 190.8), linetype = "dashed", color = "red") +
  labs(title = "Density Plot of T4Cortisol with Vertical Lines",
       x = "T4Cortisol",
       y = "Density") +
  theme_minimal()

The animals were sorted in these three categories >H = Hight >M = Medium >L = Low

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The individuals frequency distribution in theese categories are shown in the plots below

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2.2 Removing “outliers”

Observing the previous plots I tried to remove the “outliers” phenotypes above 1250, but the outcome from Shapiro test is still indicating no normality of the data.

library(tidyverse)

data_no_out <- filter(data, T4Cortisol < 1250)

# Create QQ plot
qqnorm(data_no_out$T4Cortisol, main = "QQ Plot of T4Cortisol", xlab = "Theoretical Quantiles", ylab = "Sample Quantiles")
qqline(data$T4Cortisol, col = "red")

boxplot(data_no_out$T4Cortisol, main = "Boxplot of T4 Cortisol", ylab = "T4 Cortisol")

hist(data_no_out$T4Cortisol, breaks = 20, main = "Histogram of T4 Cortisol", xlab = "T4 Cortisol")

shapiro.test(data$T4Cortisol)

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3 GENOTYPES

Lucas Alcântara sent me the path to the genotype and pedigree files: /data/cgil/daiclu/6_Data/database/public_output/bruno

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In this folder we found the following files:

I made a copy of this files in a folder called Raw_files:

/home/bambrozi/2_CORTISOL/RawFiles

This directory has two sub-directories:

4 GWAS

4.1 Checking with Phenotyped Animals also have Genotype

library(data.table)

pheno <- read.csv("/home/bambrozi/2_CORTISOL/Data/data_clean.csv")
ped <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv")
geno <- fread("/home/bambrozi/2_CORTISOL/Geno_files/genoplink.ped")
geno <- geno[,c("V2")]

#Bringing cdn_id to my phenotype file
#Generate a index with the match
matching_indices <- match(pheno$ID, ped$elora_id)
# Then, assign 'cdn_id' from 'ped' to 'pheno' where there are matches
pheno$cdn_id <- ifelse(!is.na(matching_indices), ped$cdn_id[matching_indices], NA)

#Making a phenotype file only with genotyped animals
pheno_genotyped <- pheno[pheno$cdn_id %in% geno$V2,] 

#check if all animals in this file are genotyped
checkk <- pheno_genotyped$cdn_id %in% geno$V2
sum(checkk)

4.2 Generating a Phenotype file

The phenotype file should have three columns: FID, Animal_id, Phenotype

HO <- rep("HO", 252)

pheno_gwas <- as.data.frame(cbind(HO, pheno_genotyped$cdn_id, pheno_genotyped$T4Cortisol))

colnames(pheno_gwas) <- c("FID", "cdn_id", "cortisol")

pheno_gwas$cdn_id <-  as.numeric(pheno_gwas$cdn_id)
pheno_gwas$cortisol <- round(as.numeric(pheno_gwas$cortisol),2)

write.table(pheno_gwas, "/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt", quote = F, row.names = F, col.names = T)

4.3 Adjusting the SNP_map to .map

map <- fread("/data/cgil/daiclu/6_Data/database/public_output/bruno/DGVsnpinfo.2404.ho")
morgan <- data.frame(X0 = rep(0, 45101))
mapa=as.data.frame(cbind(map$chr, map$snp_name, morgan$X0, map$location))
head(mapa)
write.table(x = mapa, file = "/home/bambrozi/2_CORTISOL/Geno_files/genoplink.map", row.names = FALSE, col.names = FALSE, sep = "\t", quote = FALSE)

4.4 Generating the bfiles

system("/home/local/bin/plink --cow --nonfounders --allow-no-sex --keep-allele-order --file /home/bambrozi/2_CORTISOL/Geno_files/genoplink --make-bed --out /home/bambrozi/2_CORTISOL/Geno_files/genoplink")
With the code above I generated the bfiles:
    genoplink.bed
  • genoplink.bim
  • genoplink.fam
  • genoplink.log
  • genoplink.nosex

5 Quality Control

We ran the code below to perfom the QC

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files/genoplink
RESULT=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean

/home/local/bin/plink \
    --bfile ${DATA} \
    --cow \
    --allow-no-sex \
    --hwe 1e-5 \
    --maf 0.01 \
    --geno 0.1 \
    --mind 0.1 \
    --keep-allele-order \
    --make-bed \
    --out ${RESULT}
    

The server screen outcome is shown below. My Image

After the Quality Control we ended up with

6 KING

To check for duplicated individuals I performed the KINSHIP analysis using one script from Larissa Braga. Running the King Analysis on Plink.

#!/bin/bash

DATA=/home/bambrozi/Extrm_ARS1_GrassHill_1/GENOTYPES/ONLY_GRASSHILL_AND_PHENO_after_QC/only_grasshill_and_pheno_clean
RESULT=/home/bambrozi/Extrm_ARS1_GrassHill_1/GENOTYPES/KING/result_king

plink2 \
    --bfile ${DATA} \
    --chr-set 29 \
    --make-king-table \
    --out ${RESULT}

This is the output screen on terminal:

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The table below is the output /home/bambrozi/2_CORTISOL/Geno_files_after_KING/result_king.kin0 and have pairwise comparisons between all individuals.

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Now we should open in R and check for individuals with more than 0,354, to perform this we can use the code below, also provided by Larissa Braga:

setwd("/home/bambrozi/2_CORTISOL/Geno_files_after_KING")

relatedness="result_king.kin0" ## change accordingly!!

library(data.table)

print(relatedness)
rel=fread(relatedness, h = T)
head(rel)

print("Individuals with different identifications above the cut off of 0.354:")
dup=subset(rel, KINSHIP >= 0.354  & IID1!=IID2)
print(dup)
nrow(dup)

So the code above will provide this output if there is any duplicated individual.

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We do not have any duplicated individual

So the file to be used are those in the directory /home/bambrozi/2_CORTISOL/Geno_files_after_QC

files:genoplink_clean

After Quality Control we didn’t lost any animal, so we don’t need to update our phenotype file

7 PCA

Now before performing the PCA analysis we need to change the FID for those individuals that has phenotype = 1 for Nadia.

#!/bin/bash

DATA=/home/bambrozi/Extrm_ARS1_GrassHill_1/PCA/imput_pca
RESULT=/home/bambrozi/Extrm_ARS1_GrassHill_1/PCA/pca_result

plink \
    --bfile ${DATA} \
    --keep-allele-order \
    --chr-set 29 \
    --pca \
    --out ${RESULT}

The PCA output:

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7.1 PCA Plot

After generate the Eigenvalues and Eigenvectors I used the code below to generate the PCA Plot

setwd("/home/bambrozi/2_CORTISOL/PCA")

library(ggplot2) 
library(tidyverse)

##
# Visualize PCA results
###

# read in result files
eigenValues <- read_delim("pca_result.eigenval", delim = " ", col_names = F)
eigenVectors <- read_delim("pca_result.eigenvec", delim = " ", col_names = F)

## Proportion of variation captured by each vector
eigen_percent <- round((eigenValues / (sum(eigenValues))*100), 2)


# PCA plot
png("pca-plink.eng.png", width=5, height=5, units="in", res=300)
ggplot(data = eigenVectors) +
  geom_point(mapping = aes(x = X3, y = X4), color = "red", shape = 19, size = 1, alpha = 1) +
  geom_hline(yintercept = 0, linetype="dotted") +
  geom_vline(xintercept = 0, linetype="dotted") +
  labs(x = paste0("Principal component 1 (", eigen_percent[1,1], " %)"),
       y = paste0("Principal component 2 (", eigen_percent[2,1], " %)")) +
  theme_minimal()
dev.off()


# PCA plot with animal ids
png("pca-plink.eng.animals_id.png", width=50, height=50, units="in", res=300)
ggplot(data = eigenVectors) +
  geom_point(mapping = aes(x = X3, y = X4), color = "red", shape = 19, size = 5, alpha = 1) +
  geom_text(mapping = aes(x = X3, y = X4, label = X2), size = 2, hjust = 0, vjust = 0) +  # Add labels for animal IDs
  geom_hline(yintercept = 0, linetype="dotted") +
  geom_vline(xintercept = 0, linetype="dotted") +
  labs(x = paste0("Principal component 1 (", eigen_percent[1,1], " %)"),
       y = paste0("Principal component 2 (", eigen_percent[2,1], " %)")) +
  theme_minimal()
dev.off()

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8 GWAS on GCTA

Previously we have performed GWAS on GCTA:

9 GWAS - EXTREME PHENO - WITH BY and SD

9.1 Data preparation

I received from Umesh a e-mail informing the three categories that the animals could be sorted based on their cortisol concentration.

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pheno <- read.table("/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt", header = T)
data_final <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits2.csv", header = T)
ids_eq <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv", header = T)


# Create an matrix with fixed effects with only those animals which also have phenotype and genotype
data_final$cdn_id <- ids_eq$cdn_id[match(data_final$ID, ids_eq$elora_id)]
fixeff <- data_final[,c("ID", "BIRTH_YEAR", "Sampling_date", "cdn_id")]
fixeff <- fixeff[fixeff$cdn_id %in% pheno$cdn_id, ]
fixeff$FID <- "HO"
fixeff <- fixeff[, c("FID", "cdn_id", "BIRTH_YEAR", "Sampling_date")]

# Now we are gona remove the intermediary animals from pheno object
pheno$Categorical <- ifelse(pheno$cortisol >= 956, "H", 
                           ifelse(pheno$cortisol <= 190.8, "L", "M"))
table(pheno$Categorical)
pheno <- pheno[pheno$Categorical != "M", ]
pheno <- pheno[, c("FID", "cdn_id", "cortisol")]

# Now we are going to remove from fixeff the animals which are not in pheno
fixeff <- fixeff[fixeff$cdn_id %in% pheno$cdn_id,]

#Checking if match the animals and order
identical(fixeff$cdn_id, pheno$cdn_id)

#Creating a file with animals to keep in the genotype file, we will use it on Plink
to_keep_geno <- pheno[, c("FID", "cdn_id")]

write.table(fixeff, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/fixeff.txt", quote = F, row.names = F, col.names = T)
write.table(pheno, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/pheno.txt", quote = F, row.names = F, col.names = T)
write.table(to_keep_geno, "/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/to_keep_geno.txt", quote = F, row.names = F, col.names = F)

We ended up with
H (Hight) = 34 animals
L (Low) = 37 animals Total = 71 animals

On Plink we will remove all individuals from genotype files that are classified as Medium, keeping only the High and Low

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/Geno_files_after_QC/genoplink_clean
RESULT=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/geno_extreme
KEEP=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/to_keep_geno.txt

plink2 --bfile ${DATA} --keep ${KEEP} --chr-set 29 --make-bed --out ${RESULT}

9.2 GWAS on GCTA

#!/bin/bash

DATA=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/geno_extreme
RESULT=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/GWAS_result
PHENO=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/pheno.txt
FIXEFF=/home/bambrozi/2_CORTISOL/GWAS/EXTREM_PHENO_BY_SD/fixeff.txt

/home/local/bin/gcta \
    --bfile ${DATA} \
    --mlma-loco \
    --pheno ${PHENO} \
    --qcovar ${FIXEFF} \
    --autosome-num 29 \
    --autosome \
    --out ${RESULT}

After ran the GWAS above I got the following message from the GCTA:

Error: analysis stopped because more than half of the variance components are constrained. The result would be unreliable. You may have a try of the option –reml-no-constrain.

As we got this error message, we needed to solve this problem, and for that we used the whole sample size (252 individuals) to estimate the variance components, and after this, using this variance components from the whole sample we performed the ssGWAS with the subset of individuals (34 High + 37 Low = 71), but this was not possible in GCTA so we switched to another software (BLUPF90+)

9.3 BLUPF90+ GWAS

To run ssGWAS on Blupf90+ suite, we will need 4 different softwares:

  • renum: just to renumerate the files and generate the renf90.par
  • preGSF90: just to perfome a quality control with different parameter from the default.
  • blupf90+: used to estimate VCE and generate Ainv and Ginv
  • postGSF90: perform GWAS

The tutorial for preGSF90 and postGSF90 we can find in the link bellow https://nce.ads.uga.edu/wiki/doku.php?id=readme.pregsf90#gwas_options_postgsf90

According to the BLUPF90+ tutorial:

ssGWAS is an iterative procedure with 2 steps:
0) Quality control
1) prediction of GEBV with ssGBLUP
2) prediction of SNP marker effects based on the GEBV

9.3.1 Files preparation

Preparing files to run Variance components estimation using REML with AI (Average Information) algorithm.

First you need to create a directory in your home directory, prepare and save the following files in:

  • Phenotype and Fixed effects file
  • Pedigree file
  • Genotype file
  • BlupF90+ executable file
  • RenumF90 executable file
  • preGSf90 executable file
  • postGSf90 executable file
  • Parameter file

      9.3.1.1 Phenotype and Fixed effects file

      The appearance of this file is like this:

      My Image

      FIRST COLUMN = Animal ID
      SECOND COLUMN = Phenotype
      THIRD COLUMN = Fixed Effect 1
      FOURTH COLUMN = Fixed Effect 2

      First we are going to generate a Phenotype_Fixed_Effect file with the whole sample (252 individuals) that we are going to use for the Variance Components Estimation.

      To get in one file these four columns we need the following code:

      fixeff <- read.table("/home/bambrozi/2_CORTISOL/GWAS/GWAS_plus_BY_Samp/fixeff.txt", header = T)
      pheno <- read.table("/home/bambrozi/2_CORTISOL/GWAS/pheno_genotyped.txt", header = T)
      ids_eq <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv", header = T)
      
      fixeff <- fixeff[, c("cdn_id", "BIRTH_YEAR", "Sampling_date")]
      pheno <- pheno[,c("cdn_id", "cortisol")]
      
      # Load necessary libraries
      library(dplyr)
      
      # Merge pheno and fixeff data frames
      merged_data <- pheno %>% 
        left_join(fixeff, by = "cdn_id")
      
      
      merged_data$iid <- ids_eq$iid[match(merged_data$cdn_id, ids_eq$cdn_id)]
      
      merged_data <- merged_data[, c("iid", "cortisol", "BIRTH_YEAR", "Sampling_date")]
      
      write.table(merged_data, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/fenofix.txt", col.names = F, quote = F, row.names = F)

      The file should be saved as text file, with separation by space and no columns names.

      PS: if there are any NA, they sould be replaced by 9999

      9.3.1.2 Pedigree file

      The appearance of this file is like this:

      My Image

      FIRST COLUMN = Animal ID
      SECOND COLUMN = Sire ID
      THIRD COLUMN = Dam ID

      The file should be saved as text file, with separation by space and no columns names.

      We used the code below to remove the commas of a .csv file to a file with sepation by spaces.

      # to replace comma for space in the .csv file with the equivalence among IDs
      sed -i 's/,/ /g' bruno_ids.csv

      9.3.1.3 Genotype file

      First we are going to generate a Genotype file with the whole sample (252 individuals) that we are going to use for the Variance Components Estimation.

      The appearance of this file is like this:

      My Image

      FIRST COLUMN = Animal ID SECOND COLUMN = Genotypes (0, 1 and 2 format)

      The file should be saved as text file, with separation by space and no columns names.

      We used the code below to replace the cid for iid. First we merge using the second column of the firs file, and the first column of the second file. Then we use again the command awk to keep only the third and fifth columsn and sabe in a different object.

      # Using the awk function to merge the two files and the second awk to select only the 3rd and 5fh columns
      awk 'FNR==NR {a[$2]=$0; next} {print a[$1], $0}' bruno_ids.csv bruno_gntps.txt | awk '{print$3,$5}' > bruno_gntps_iid

      Below we can find the file’s location from the code above: /home/bambrozi/2_CORTISOL/RawFiles/Genotypes/bruno_gntps.txt /home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/bruno_gntps_iid

9.3.2 Download the executable files

Download from this website https://nce.ads.uga.edu/html/projects/programs/Linux/64bit/:
  • BlupF90+ = we will use to estimate the Variance components and GEBVs
  • renumF90 = we will use to renumerate the files
  • preGSf90 = we will use to perform the Quality control
  • postGSf90 = we will use for GWAS

9.3.3 SNP MAP

mapfile <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/DGVsnpinfo.2404.ho", header = T)

colnames(mapfile)

colnames(mapfile) <- c("SNP_ID", "CHR", "POS")

mapfile <- mapfile[,c("CHR", "POS", "SNP_ID")]

write.table(mapfile, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/snpmap.txt", col.names = T, row.names = F, quote = F)

9.3.4 Parameter file for Quality Control

But, before running the GEBV we will first perform one additional step to “CLEAN” our genotypes. Actually BLUPF90 by default perform a data cleaning with pre set parameters, but as HWE is not used by default, we will perform this additional step.

The Parameter card for this step is the parameter bellow:

/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/renum_QC.par


DATAFILE
fenofix.txt
TRAITS
2
FIELDS_PASSED TO OUTPUT

WEIGHT(S)

RESIDUAL_VARIANCE
1.0
EFFECT
3 cross numer
EFFECT
4 cross alpha
EFFECT
1 cross alpha
RANDOM
animal
FILE
bruno_ped_iid_blup.txt
FILE_POS
1 2 3 0 0
SNP_FILE
bruno_gntps_iid
PED_DEPTH
0
(CO)VARIANCES
1.0
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31
  • DATAFILE: bellow this line you need to inform the name of the file with phenotype and fixed effects. As before running BLUPF90 on server you are going to direct the terminal to the directory where all these files are placed you only need to inform the name.
  • TRAITS: below this line you need to inform which column are the phenotype date in the previous file, in this example, 2.
  • FIELDS_PASSED TO OUTPUT:
  • WEIGHT (S):
  • RESIDUAL VARIANCE: for the firs run you need to inform the value of 1.0, for the second you can pick the variance from the firs run’s output.
  • EFFECT: you will inform your first effect, in this example, Birth Year, which is in the column 3, and the word cross numer because is a number.
  • EFFECT: you should provide the next effect, in this example, sample date, as sample date has one non numeric character you should inform as cross alpha, in this example column 4.
  • EFFECT: now I’m providing my animal ID information, in this example column 1, and again cross alpha because has number and letters in the ID. I’m also informing that this effect is RANDOM, and that is my animal effect.
  • FILE: bellow this line I need to provide the pedigree file. Again, as I’m already in the directory which contain the pedigree file I only need to provide the file name.
  • FILE-POS: Here I’ll inform which columns should be considered in the pedigree file, in this situation, 1 2 3 0 0.
  • PED_DEPTH: Now we can inform the depth we want the software considers the pedigree, or if we leave 0 it will the maximum possible.
  • (CO) VARIANCES: Here you should provide the Variance/Co-variance matrix, like as for residual variance in the first run we set up to 0 in this example that we don´t have to imagine any co-variance, but if you know that exist variance among you effects you shoul set up XXX for ….
  • OPTION outcallrate: Save the call rate information on SNP markers in the file.
  • OPTION saveCleanSNPs: This option generates 4 new files. We assume snpfile as a marker file.
    • snpfile_clean = new SNP marker file.
    • snpfile_clean_XrefID = new cross-reference file.
    • snpfile_SNPs_removed = a list of removed markers.
    • snpfile_Animals_removed = a list of removed animals.
  • OPTION minfreq 0.01: Minimum allele frequency to retain the marker.
  • OPTION map_file snpmap.txt: This option will upload the SNP MAP
  • OPTION excludeCHR 30 31: This option will remove sexual chromosome that is the 30 and 31

To run any softwere from Blupf90 suit we will perform always in this way:

  1. Go to the server you wanna run this analysis, for instance, grand
ssh grand
  1. Now go to the directory you have created to run this analysis where that set of files are placed.
cd /home/bambrozi/2_CORTISOL/GWAS/BLUPF90
  1. Make the renumF90 and BlupF90+ files executables
chmod +x renumf90
chmod +x blupf90+
  1. Run renumF90
./renumf90

When you run the code above, it will ask you the name of your parameter card, for this step is renum_QC.par.

The command above will generate couple files, among them renf90.par

We modified renf90.par in:
  • renf90_DataClean.par
  • renf90_VarCompEst.par

The parameter card to perform the Quality Control is: /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/renf90_DataClean.par

It will be run using the software pre preGS90 to generate the Clean Genotype and SNP_MAP files after Quality Control.

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        23 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   1.0000    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   1.0000    
OPTION SNP_file bruno_gntps_iid
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31

The second parameter card used for Variance Components Estimation (VCE) is the following: /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/renf90_VarCompEst.par

It will be run using the software pre blupf90+ to generate the VCE.

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        23 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   1.0000    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   1.0000    
OPTION SNP_file bruno_gntps_iid_clean
OPTION no_quality_control
OPTION method VCE
OPTION origID
OPTION missing 9999
OPTION se_covar_function H2_1 g_3_3_1_1/(g_3_3_1_1+r_1_1)
  • OPTION SNP_file bruno_gntps_iid_clean: we are going to inform the genotype file generated in the previous step (the Quality Control). Blup will create an file with the same name that the original genotype file, and add the sufix **_clean**
  • OPTION no_quality_control we need to set up this option because we performed Quality Control in the previous step and now we don’t need that Blupf90+ perform again. Blupf90+ by default perform quality control, so if we do not want, we need to specify.
  • OPTION method: VCE (Variance Component Estimation).
  • OPTION OrigID: this will keep the original ID informed.
  • OPTION missing 9999: you are informing that missing values will appear as 9999
  • OPTION se_covar_function: H2_1 g_3_3_1_1/(g_3_3_1_1+r_1_1)
    • H2_1: the name that your function will appear on the output files.
    • g_3_3: you are asking for genetic variance estimation for the 3rd informed effect.
    • **_1_1**: this effect is in the 1st column.
    • /(g_3_3_1_1+r_1_1): to get the total phenotipic variance, you are summing to genetic variance the residual variance of the effect in column 1.

In the parameter card above, we remove the option for Quality Control and added options for Variance Components Estimation, for Missing data, for origID and for heritability estimation, but the MOST IMPORTANT PART is we need to change the OPTION SNP_FILE, replacing the original genotype file, for the “clean” version generated in the previous step.

The Variance Components will be placed in the file: /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/blupf90.log

blupf90.log
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Now you should update you renf90_VarCompEst.par file with these informations from the .log file

Copy Residual Variance from blupf90.log and will paste on renf90_VarCompEst.par RANDOM_RESIDUAL_VALUES Copy Genetic variance for effect x from blupf90.log and will paste on renf90_VarCompEst.par (CO) VARIANCE

If the Residual Variance and Genetic variance for effect x didn’t change in your blupf90.log the analysis ended, but if this value vary, you should update again the renf90.par and run again blupf90+ until this values don’t change more.

blupf90.log
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Parameter card
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Now that we have the Variance components we go for the next step:
  • Prediction of SNP marker effects based on the GEBV
  • GWAS for High and Low cortisol animals
To do this, I’ll:
  • generate the Phenotype_FixEff file like bellow
  • generate the genotype file like bellow
  • Perform the QUALITY CONTROL for the new (71 samples) genotype file
  • Add the VCE from the 252 data set in the parameter card
  • Generate the GEBV
  • Run GWAS

9.3.5 Prediction of SNP marker effects based on the GEBV

9.3.6 GWAS for High and Low cortisol animals

Now we’ll run a new analysis using the Variance Components Estimation from the previous step to perform the GWAS.

To perform this first we need a Phenotype file, and a Genotype file only with the 71 animals (High=34 and Low=37)

9.3.6.1 Updating the files

9.3.6.1.1 Phenotype and Fixed Effects files

I used the code bellow to remove individuals with MEDIUM cortisol Phenotype

phenofix <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/fenofix.txt")


# Now we are gona remove the intermediary animals from pheno object
phenofix$Categorical <- ifelse(phenofix$V2 >= 956, "H", 
                            ifelse(phenofix$V2 <= 190.8, "L", "M"))
table(phenofix$Categorical)

phenofix <- phenofix[phenofix$Categorical != "M", ]

phenofix <- phenofix[, c("V1", "V2", "V3", "V4")]

write.table(phenofix, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/fenofix.txt", col.names = F, row.names = F, quote = F)
9.3.6.1.2 Genotype files

I used this command line bellow to remove the individuals with MEDIUM phenotypes.

awk 'NR==FNR{ids[$1]; next} $1 in ids' fenofix.txt bruno_gntps_iid > bruno_gntps_iid_71

9.3.6.2 Running renum_QC.par

/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/renum_QC.par

DATAFILE
fenofix.txt
TRAITS
2
FIELDS_PASSED TO OUTPUT

WEIGHT(S)

RESIDUAL_VARIANCE
77182
EFFECT
3 cross numer
EFFECT
4 cross alpha
EFFECT
1 cross alpha
RANDOM
animal
FILE
bruno_ped_iid_blup.txt
FILE_POS
1 2 3 0 0
SNP_FILE
bruno_gntps_iid_71
PED_DEPTH
0
(CO)VARIANCES
28212
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31
We modified renf90.par in 3 copies:
  • renf90_DataClean.par
  • renf90_ssGWAS1_Ginv.par
  • renf90_ssGWAS2_SNPeff.par

9.3.6.3 Running renf90_DataClean.par for Quality Control

To run the parameter card bellow we are going to use the software presGSf90 /home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/renf90_DataClean.par

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71
OPTION outcallrate
OPTION saveCleanSNPs
OPTION minfreq 0.01
OPTION map_file snpmap.txt
OPTION excludeCHR 30 31

9.3.6.4 Running renf90_ssGWAS1_Ginv.par for Ginv estimation

May be necessary to run the command bellow on the server Setting the stack size to “unlimited” allows the program to allocate memory for these large structures without hitting stack limits. By removing stack size limits, BLUPF90 is less likely to encounter segmentation faults or memory allocation issues that arise when the stack space is insufficient for the computations needed.

ulimit -s unlimited

The parameter card bellow we are going to run using the software blupf90+:

/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/renf90_ssGWAS1_Ginv.par

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71_clean
OPTION no_quality_control
OPTION origID
OPTION missing 9999
OPTION saveGInverse
OPTION saveA22Inverse
OPTION snp_p_value

9.3.6.5 Running renf90_ssGWAS2_SNPeff.par for GWAS

The parameter card bellow we are going to run using the software postGSf90:

# BLUPF90 parameter file created by RENUMF90
DATAFILE
 renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71_clean
OPTION origID
OPTION no_quality_control
OPTION readGInverse
OPTION readA22Inverse
OPTION map_file snpmap.txt_clean
OPTION snp_p_value
OPTION Manhattan_plot_R
OPTION Manhattan_plot 
This code will generate several files, among them:
  • chrsnp_pval:
    • Column 1: trait
    • Column 2: effect
    • Column 3: -log10(p-value)
    • Column 4: SNP
    • Column 5: Chromosome
    • Column 6: Position in bp
  • Pft1e3.R: a r-code to generate the Manhattan plot in R using the chrsnp_pval

9.3.6.6 Manhattan Plots for BLUPF90 GWAS

9.3.6.6.1 Genome Independent Segment

To make the Manhattan Plot considering Genome Independent Segment we should run the code bellow. This code has part of the code in the file Pft1e3.R

Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)

# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8

# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)

NeL <- Ne*L_M

# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)


# Calculate Bonferroni threshold (already done)
bonf <- -log10(0.05 / Me)


setwd("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO")
# Read in and process data for Manhattan plot
yyy1 <- read.table("chrsnp_pval")
yyy <- yyy1[order(yyy1$V4), ]
zzz <- yyy[which(yyy$V1 == 1 & yyy$V2 == 3), ]
n <- nrow(zzz)
y <- zzz[, 4]
x <- zzz[, 3]
chr1 <- zzz[, 5]
chr <- NULL
pos <- NULL

for (i in unique(yyy$V5)) {
  zz <- yyy[yyy$V5 == i, ]
  key <- zz$V4
  medio <- round(nrow(zz) / 2, 0)
  z <- key[medio]
  pos <- c(pos, z)
}

chrn <- unique(yyy$V5)
one <- which(chr1 %% 4 == 0)
two <- which(chr1 %% 4 == 1)
three <- which(chr1 %% 4 == 2)
four <- which(chr1 %% 4 == 3)
chr[one] <- "darkgoldenrod"
chr[two] <- "darkorchid"
chr[three] <- "blue"
chr[four] <- "forestgreen"

# Create Manhattan plot with Bonferroni line and legend
pdf(file = "Pft1e3_manplot_with_bonf_ind_seg.pdf", family = "sans", height = 27.8, width = 50, pointsize = 20, bg = "white")
par(mfrow = c(1, 1), family = "sans", cex = 1.5, font = 2)
plot(y, x, xaxt = "n", main = "Manhattan Plot SNP p_value - Trait: 1 Effect: 3", xlab = "", ylab = "-log10(p-value)", pch = 20, xlim = c(1, n), ylim = c(0, max(x)), col = chr)

# Add Bonferroni line
abline(h = bonf, col = "red", lwd = 2, lty = 2)

# Add legend for Bonferroni line
legend("topright", legend = "Bonferroni correction for genome independent segments", col = "red", lwd = 2, lty = 2, cex = 1)

axis(1, at = pos, labels = chrn)
dev.off()

My Image

9.3.6.7 Get rsID

For additional analysis like Variant Effect Prediction (VEP) we need the rsID, to get the rsID we use the software SNPChimp which requires SNP_names, but the BLUPF90 output have only the Chromosome and Position of the SNPs, so we are going to perfome these two steps to get one file with t he significant SNPs + SNP_name + rsID

9.3.6.7.1 Step 01 = Bring the SNP name to GWAS output

For this analysis we have to build this new sheet bringing SNP ID from snpmap.txt

gwas = read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/chrsnp_pval")
colnames(gwas) <- c("V1", "V2", "LOG_P", "SNP", "CHR", "BP")
gwas <- filter(gwas, LOG_P >= bonf)

snpmap <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/snpmap.txt", header = T)

# Filter snpmap to only include rows that match the CHR and BP values in out_genes
filtered_snpmap <- snpmap[snpmap$CHR %in% gwas$CHR & snpmap$POS %in% gwas$BP, ]

# Merge the filtered snpmap with out_genes
gwas_snpname <- merge(gwas, filtered_snpmap[, c("CHR", "POS", "SNP_ID")], 
                   by.x = c("CHR", "BP"), by.y = c("CHR", "POS"), 
                   all.x = TRUE)

gwas_snpname <- gwas_snpname[,c("CHR", "BP", "LOG_P", "SNP_ID")]

write.csv(gwas_snpname, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/chrsnp_pval_SNPid_ind_seg_sig_BLUPF90.csv")
X CHR BP LOG_P SNP_ID
1 11 19779915 4.828190 Hapmap55558-rs29013980
2 12 17477322 4.417576 BTB-00488482
3 14 15929822 5.467439 ARS-BFGL-NGS-82859
4 15 33066384 5.041271 BTB-00594449
5 15 57930281 4.514377 ARS-BFGL-NGS-30515
6 17 46487068 4.547766 ARS-BFGL-NGS-12510
7 17 47884497 6.622354 ARS-BFGL-NGS-87412
8 17 48800954 5.268940 ARS-BFGL-NGS-112149
9 2 118820218 4.420962 Hapmap53065-rs29026778
10 2 41602429 4.497977 BTB-00096979
11 2 41670655 4.377284 Hapmap48777-BTA-47434
12 20 60365668 4.603817 BTB-01341053
13 20 65351653 4.891206 ARS-BFGL-NGS-91119
14 21 4055731 4.493938 ARS-BFGL-NGS-112210
15 23 44822126 4.316434 ARS-BFGL-NGS-115605
16 24 857728 4.458018 Hapmap47669-BTA-59022
17 28 20016672 5.130557 ARS-BFGL-NGS-116552
18 28 35807366 4.695875 ARS-BFGL-NGS-71077
19 29 33039863 4.933140 ARS-BFGL-NGS-41631
20 3 110866523 4.815943 ARS-BFGL-NGS-118207
21 3 111526170 4.760351 ARS-BFGL-NGS-74948
22 3 111730561 4.390707 BTB-01641394
23 3 111751663 4.390707 ARS-BFGL-NGS-37809
24 3 111772736 5.088738 ARS-BFGL-NGS-25298
25 4 103979600 5.591006 ARS-BFGL-NGS-110705
26 4 107787202 5.375628 ARS-BFGL-NGS-45265
27 4 109568557 4.709882 Hapmap48062-BTA-72409
28 4 110053134 5.245598 UA-IFASA-2147
29 4 24239851 4.641241 Hapmap60681-rs29013301
30 4 25933587 4.352451 Hapmap50554-BTA-107048
31 4 26511448 4.537662 Hapmap59011-rs29027498
32 4 27021431 5.045447 Hapmap59743-rs29017061
33 4 27094376 5.485063 BTB-00170785
34 4 50755462 5.015905 ARS-BFGL-NGS-12139
35 4 7873471 4.610767 Hapmap60503-rs29018741
36 4 95650788 4.825103 Hapmap58854-rs29023486
37 6 36184467 4.758110 Hapmap23854-BTC-062412
38 7 110306791 4.609691 BTB-01148543
9.3.6.7.2 Step 02 = Bring the rsID to the file with SNP name.

After get the rsID from SNPCHIMP I produced this table with the code bellow:

rsid <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/SNPchimp_result_1723415732_BLUPF90GWAS.tsv", header = T)

merged <- merge(rsid, gwas_snpname, by.x ="SNP_name", by.y ="SNP_ID")

colnames(merged)

merged <- merged[,c("SNP_name", "rs", "CHR", "BP", "LOG_P")]

colnames(merged) <- c("SNP_name", "rsID", "CHR", "BP", "LOG_P")

write.csv(merged, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/gwas_ind_seg_sig_SNPname_rsID.csv")
X SNP_name rsID CHR BP LOG_P
1 ARS-BFGL-NGS-110705 rs110079750 4 103979600 5.591006
2 ARS-BFGL-NGS-112149 rs109276211 17 48800954 5.268940
3 ARS-BFGL-NGS-112210 rs109631116 21 4055731 4.493938
4 ARS-BFGL-NGS-115605 rs42029843 23 44822126 4.316434
5 ARS-BFGL-NGS-116552 rs110428837 28 20016672 5.130557
6 ARS-BFGL-NGS-118207 rs110081798 3 110866523 4.815943
7 ARS-BFGL-NGS-12139 rs110160157 4 50755462 5.015905
8 ARS-BFGL-NGS-12510 rs110038841 17 46487068 4.547766
9 ARS-BFGL-NGS-25298 rs109868537 3 111772736 5.088738
10 ARS-BFGL-NGS-30515 rs110565206 15 57930281 4.514377
11 ARS-BFGL-NGS-37809 rs42751504 3 111751663 4.390707
12 ARS-BFGL-NGS-41631 rs110121846 29 33039863 4.933140
13 ARS-BFGL-NGS-45265 rs110935391 4 107787202 5.375628
14 ARS-BFGL-NGS-71077 rs109584097 28 35807366 4.695875
15 ARS-BFGL-NGS-74948 rs41585925 3 111526170 4.760351
16 ARS-BFGL-NGS-82859 rs110506037 14 15929822 5.467439
17 ARS-BFGL-NGS-87412 rs109273103 17 47884497 6.622354
18 ARS-BFGL-NGS-91119 rs109575643 20 65351653 4.891206
19 BTB-00096979 rs43305418 2 41602429 4.497977
20 BTB-00170785 rs43377276 4 27094376 5.485063
21 BTB-00488482 rs43691687 12 17477322 4.417576
22 BTB-00594449 rs41764450 15 33066384 5.041271
23 BTB-01148543 rs42305073 7 110306791 4.609691
24 BTB-01341053 rs42462935 20 60365668 4.603817
25 BTB-01641394 rs42752353 3 111730561 4.390707
26 Hapmap23854-BTC-062412 rs81154019 6 36184467 4.758110
27 Hapmap47669-BTA-59022 rs41645754 24 857728 4.458018
28 Hapmap48062-BTA-72409 rs41566051 4 109568557 4.709882
29 Hapmap48777-BTA-47434 rs41636137 2 41670655 4.377284
30 Hapmap50554-BTA-107048 rs41615935 4 25933587 4.352451
31 Hapmap53065-rs29026778 rs29026778 2 118820218 4.420962
32 Hapmap55558-rs29013980 rs29013980 11 19779915 4.828190
33 Hapmap58854-rs29023486 rs29023486 4 95650788 4.825103
34 Hapmap59011-rs29027498 rs29027498 4 26511448 4.537662
35 Hapmap59743-rs29017061 rs29017061 4 27021431 5.045447
36 Hapmap60503-rs29018741 rs29018741 4 7873471 4.610767
37 Hapmap60681-rs29013301 rs29013301 4 24239851 4.641241
38 UA-IFASA-2147 rs29012492 4 110053134 5.245598

9.3.7 BLUPF90+ GALLO

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#import MARKER files = the GWAS output
gwas = read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/gwas_ind_seg_sig_SNPname_rsID.csv")
colnames(gwas) <- c("X", "SNP", "rsID", "CHR", "BP", "LOG_P")


#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_genes_50k_pvalue.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_qtl_50k_pvalue.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("SNP", "rsID", "CHR", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_qtl_50k_pvalue_clean.csv")

The GALLO output are bellow:

For GENES

X.1 X SNP rsID CHR BP LOG_P chr start_pos end_pos width strand gene_id gene_name gene_biotype
1 34 Hapmap59011-rs29027498 rs29027498 4 26511448 4.537662 4 26324012 26469689 145678 - ENSBTAG00000014074 SNX13 protein_coding
2 20 BTB-00170785 rs43377276 4 27094376 5.485063 4 27093041 27667961 574921 + ENSBTAG00000003808 HDAC9 protein_coding
3 33 Hapmap58854-rs29023486 rs29023486 4 95650788 4.825103 4 95631850 95631921 72 - ENSBTAG00000045386 bta-mir-320b miRNA
4 1 ARS-BFGL-NGS-110705 rs110079750 4 103979600 5.591006 4 103877533 103947944 70412 - ENSBTAG00000004799 DENND2A protein_coding
5 1 ARS-BFGL-NGS-110705 rs110079750 4 103979600 5.591006 4 103994401 104008963 14563 + ENSBTAG00000020484 ADCK2 protein_coding
6 1 ARS-BFGL-NGS-110705 rs110079750 4 103979600 5.591006 4 104013524 104019157 5634 + ENSBTAG00000021759 NDUFB2 protein_coding
7 1 ARS-BFGL-NGS-110705 rs110079750 4 103979600 5.591006 4 104027627 104194547 166921 - ENSBTAG00000021761 BRAF protein_coding
8 13 ARS-BFGL-NGS-45265 rs110935391 4 107787202 5.375628 4 107701708 108020989 319282 + ENSBTAG00000054424 NA protein_coding
9 13 ARS-BFGL-NGS-45265 rs110935391 4 107787202 5.375628 4 107718449 108101163 382715 - ENSBTAG00000017676 TPK1 protein_coding
10 28 Hapmap48062-BTA-72409 rs41566051 4 109568557 4.709882 4 109526182 109526350 169 - ENSBTAG00000053666 NA misc_RNA
11 36 Hapmap60503-rs29018741 rs29018741 4 7873471 4.610767 4 7867490 7867588 99 - ENSBTAG00000045389 U6 snRNA
12 7 ARS-BFGL-NGS-12139 rs110160157 4 50755462 5.015905 4 50743789 50957593 213805 - ENSBTAG00000006589 CFTR protein_coding
13 8 ARS-BFGL-NGS-12510 rs110038841 17 46487068 4.547766 17 46406767 46715519 308753 + ENSBTAG00000009797 RIMBP2 protein_coding
14 17 ARS-BFGL-NGS-87412 rs109273103 17 47884497 6.622354 17 47426664 48084896 658233 + ENSBTAG00000002950 TMEM132D protein_coding
15 2 ARS-BFGL-NGS-112149 rs109276211 17 48800954 5.268940 17 48417339 48868560 451222 - ENSBTAG00000046256 TMEM132C protein_coding
16 3 ARS-BFGL-NGS-112210 rs109631116 21 4055731 4.493938 21 3866347 4146441 280095 - ENSBTAG00000013422 GABRB3 protein_coding
17 4 ARS-BFGL-NGS-115605 rs42029843 23 44822126 4.316434 23 44802116 44835671 33556 - ENSBTAG00000010256 TMEM170B protein_coding
18 14 ARS-BFGL-NGS-71077 rs109584097 28 35807366 4.695875 28 35805434 35823873 18440 + ENSBTAG00000021416 PRXL2A protein_coding
19 14 ARS-BFGL-NGS-71077 rs109584097 28 35807366 4.695875 28 35848163 35904370 56208 + ENSBTAG00000003907 TSPAN14 protein_coding
20 6 ARS-BFGL-NGS-118207 rs110081798 3 110866523 4.815943 3 110769753 110827938 58186 + ENSBTAG00000013867 DLGAP3 protein_coding
21 6 ARS-BFGL-NGS-118207 rs110081798 3 110866523 4.815943 3 110835276 110839536 4261 + ENSBTAG00000014235 SMIM12 protein_coding
22 6 ARS-BFGL-NGS-118207 rs110081798 3 110866523 4.815943 3 110888198 110890904 2707 - ENSBTAG00000013881 GJA4 protein_coding
23 6 ARS-BFGL-NGS-118207 rs110081798 3 110866523 4.815943 3 110900183 110905849 5667 - ENSBTAG00000012584 GJB3 protein_coding
24 15 ARS-BFGL-NGS-74948 rs41585925 3 111526170 4.760351 3 111552563 111568286 15724 - ENSBTAG00000020798 C3H1orf94 protein_coding
25 25 BTB-01641394 rs42752353 3 111730561 4.390707 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
26 11 ARS-BFGL-NGS-37809 rs42751504 3 111751663 4.390707 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
27 9 ARS-BFGL-NGS-25298 rs109868537 3 111772736 5.088738 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
28 22 BTB-00594449 rs41764450 15 33066384 5.041271 15 32820702 33050885 230184 - ENSBTAG00000054820 NA lncRNA
29 12 ARS-BFGL-NGS-41631 rs110121846 29 33039863 4.933140 29 32993975 33024632 30658 - ENSBTAG00000003176 JAM3 protein_coding
30 12 ARS-BFGL-NGS-41631 rs110121846 29 33039863 4.933140 29 33036823 33044208 7386 - ENSBTAG00000052082 NA lncRNA
31 16 ARS-BFGL-NGS-82859 rs110506037 14 15929822 5.467439 14 15865228 15995482 130255 - ENSBTAG00000013537 FER1L6 protein_coding
32 16 ARS-BFGL-NGS-82859 rs110506037 14 15929822 5.467439 14 15914441 15917042 2602 + ENSBTAG00000048373 NA protein_coding
33 18 ARS-BFGL-NGS-91119 rs109575643 20 65351653 4.891206 20 65311530 65343117 31588 - ENSBTAG00000009401 MTRR protein_coding
34 18 ARS-BFGL-NGS-91119 rs109575643 20 65351653 4.891206 20 65343172 65354775 11604 + ENSBTAG00000009400 FASTKD3 protein_coding
35 18 ARS-BFGL-NGS-91119 rs109575643 20 65351653 4.891206 20 65368557 65384042 15486 + ENSBTAG00000055034 CFAP90 protein_coding
36 18 ARS-BFGL-NGS-91119 rs109575643 20 65351653 4.891206 20 65388705 65842986 454282 - ENSBTAG00000019210 ADCY2 protein_coding
37 31 Hapmap53065-rs29026778 rs29026778 2 118820218 4.420962 2 118803760 118824701 20942 + ENSBTAG00000030718 SPATA3 protein_coding
38 31 Hapmap53065-rs29026778 rs29026778 2 118820218 4.420962 2 118831062 118836777 5716 + ENSBTAG00000054626 NA lncRNA
39 31 Hapmap53065-rs29026778 rs29026778 2 118820218 4.420962 2 118846915 118854132 7218 + ENSBTAG00000026710 C2H2orf72 protein_coding
40 31 Hapmap53065-rs29026778 rs29026778 2 118820218 4.420962 2 118865174 118946608 81435 + ENSBTAG00000005119 PSMD1 protein_coding
41 29 Hapmap48777-BTA-47434 rs41636137 2 41670655 4.377284 2 41676135 42259624 583490 - ENSBTAG00000005562 GALNT13 protein_coding
42 23 BTB-01148543 rs42305073 7 110306791 4.609691 7 110296879 110565532 268654 + ENSBTAG00000035662 CAMK4 protein_coding
43 26 Hapmap23854-BTC-062412 rs81154019 6 36184467 4.758110 6 36052150 36198478 146329 - ENSBTAG00000010120 HERC3 protein_coding
44 27 Hapmap47669-BTA-59022 rs41645754 24 857728 4.458018 24 779627 866891 87265 - ENSBTAG00000000656 NFATC1 protein_coding
45 27 Hapmap47669-BTA-59022 rs41645754 24 857728 4.458018 24 879074 1029386 150313 - ENSBTAG00000001224 ATP9B protein_coding
46 32 Hapmap55558-rs29013980 rs29013980 11 19779915 4.828190 11 19680235 19765027 84793 - ENSBTAG00000001114 PRKD3 protein_coding
47 32 Hapmap55558-rs29013980 rs29013980 11 19779915 4.828190 11 19792170 19822758 30589 + ENSBTAG00000013923 QPCT protein_coding

FOR QTLs

X SNP rsID CHR QTL_type start_pos end_pos QTL_ID
1 Hapmap60503-rs29018741 rs29018741 4 Milk 7839854 7839858 118382
2 Hapmap60503-rs29018741 rs29018741 4 Milk 7851689 7851693 118383
3 Hapmap60503-rs29018741 rs29018741 4 Milk 7878682 7878686 113676
4 Hapmap60503-rs29018741 rs29018741 4 Milk 7878682 7878686 118072
5 Hapmap60681-rs29013301 rs29013301 4 Meat_and_Carcass 24248260 24248264 228128
6 Hapmap60681-rs29013301 rs29013301 4 Reproduction 24279008 24279012 212516
7 Hapmap60681-rs29013301 rs29013301 4 Meat_and_Carcass 24288010 24288014 107768
8 Hapmap60681-rs29013301 rs29013301 4 Meat_and_Carcass 24288010 24288014 107796
9 Hapmap59011-rs29027498 rs29027498 4 Exterior 26511446 26511450 66124
10 Hapmap59743-rs29017061 rs29017061 4 Production 27014514 27014518 65173
11 ARS-BFGL-NGS-12139 rs110160157 4 Meat_and_Carcass 50781007 50781011 228112
12 ARS-BFGL-NGS-110705 rs110079750 4 Health 103949847 103949851 96218
13 ARS-BFGL-NGS-110705 rs110079750 4 Meat_and_Carcass 104013536 104013540 231600
14 ARS-BFGL-NGS-45265 rs110935391 4 Reproduction 107749162 107749166 106782
15 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46449873 46449877 162016
16 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46449873 46449877 163641
17 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46452018 46452022 162017
18 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46452018 46452022 163642
19 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46455275 46455279 163230
20 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46455275 46455279 163643
21 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46457135 46457139 163231
22 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46457135 46457139 163644
23 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46460742 46460746 163159
24 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46460742 46460746 163583
25 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46463937 46463941 163232
26 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46463937 46463941 163645
27 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46474328 46474332 162015
28 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46474328 46474332 163640
29 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46474997 46475001 162014
30 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46474997 46475001 163639
31 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46476243 46476247 162013
32 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46476243 46476247 163638
33 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46476816 46476820 162012
34 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46476816 46476820 163637
35 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46480499 46480503 161948
36 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46480499 46480503 162037
37 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46481110 46481114 161941
38 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46481110 46481114 162030
39 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46481709 46481713 162019
40 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46481709 46481713 162100
41 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46482674 46482678 162020
42 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46482674 46482678 162101
43 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46483484 46483488 162021
44 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46483484 46483488 162102
45 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46529284 46529288 163162
46 ARS-BFGL-NGS-12510 rs110038841 17 Milk 46529284 46529288 163584
47 ARS-BFGL-NGS-87412 rs109273103 17 Milk 47837023 47837027 162764
48 ARS-BFGL-NGS-87412 rs109273103 17 Milk 47844960 47844964 162876
49 ARS-BFGL-NGS-87412 rs109273103 17 Milk 47851806 47851810 162921
50 ARS-BFGL-NGS-87412 rs109273103 17 Milk 47855252 47855256 162648
51 ARS-BFGL-NGS-87412 rs109273103 17 Production 47856400 47856404 23869
52 ARS-BFGL-NGS-87412 rs109273103 17 Production 47856400 47856404 68904
53 ARS-BFGL-NGS-87412 rs109273103 17 Exterior 47856400 47856404 102064
54 ARS-BFGL-NGS-87412 rs109273103 17 Exterior 47884495 47884499 102065
55 ARS-BFGL-NGS-87412 rs109273103 17 Milk 47924476 47924480 163030
56 ARS-BFGL-NGS-87412 rs109273103 17 Milk 47924476 47924480 163517
57 ARS-BFGL-NGS-87412 rs109273103 17 Milk 47926919 47926923 162858
58 ARS-BFGL-NGS-87412 rs109273103 17 Milk 47926919 47926923 163422
59 ARS-BFGL-NGS-112149 rs109276211 17 Milk 48759668 48759672 63966
60 ARS-BFGL-NGS-112210 rs109631116 21 Reproduction 4074797 4074801 145524
61 ARS-BFGL-NGS-112210 rs109631116 21 Reproduction 4079204 4079208 145458
62 ARS-BFGL-NGS-112210 rs109631116 21 Reproduction 4079247 4079251 145445
63 ARS-BFGL-NGS-112210 rs109631116 21 Reproduction 4085667 4085671 145389
64 ARS-BFGL-NGS-112210 rs109631116 21 Reproduction 4085667 4085671 146710
65 ARS-BFGL-NGS-112210 rs109631116 21 Reproduction 4085817 4085821 146703
66 ARS-BFGL-NGS-115605 rs42029843 23 Milk 44787659 44787663 254748
67 ARS-BFGL-NGS-115605 rs42029843 23 Milk 44789048 44789052 254706
68 ARS-BFGL-NGS-115605 rs42029843 23 Meat_and_Carcass 44807487 44807491 226965
69 ARS-BFGL-NGS-115605 rs42029843 23 Meat_and_Carcass 44807487 44807491 229458
70 ARS-BFGL-NGS-115605 rs42029843 23 Meat_and_Carcass 44807487 44807491 234754
71 ARS-BFGL-NGS-115605 rs42029843 23 Milk 44808987 44808991 254767
72 ARS-BFGL-NGS-115605 rs42029843 23 Milk 44830780 44830784 254833
73 ARS-BFGL-NGS-115605 rs42029843 23 Milk 44834127 44834131 254725
74 ARS-BFGL-NGS-115605 rs42029843 23 Milk 44845256 44845260 254688
75 ARS-BFGL-NGS-115605 rs42029843 23 Milk 44848146 44848150 254689
76 ARS-BFGL-NGS-115605 rs42029843 23 Milk 44859653 44859657 254769
77 ARS-BFGL-NGS-116552 rs110428837 28 Milk 20016670 20016674 112226
78 ARS-BFGL-NGS-116552 rs110428837 28 Milk 20016670 20016674 118869
79 ARS-BFGL-NGS-116552 rs110428837 28 Milk 20017763 20017767 112227
80 ARS-BFGL-NGS-116552 rs110428837 28 Milk 20017763 20017767 118870
81 ARS-BFGL-NGS-116552 rs110428837 28 Milk 20045011 20045015 118664
82 ARS-BFGL-NGS-116552 rs110428837 28 Milk 20060184 20060188 118665
83 ARS-BFGL-NGS-71077 rs109584097 28 Reproduction 35807364 35807368 138601
84 ARS-BFGL-NGS-71077 rs109584097 28 Reproduction 35807364 35807368 138602
85 ARS-BFGL-NGS-118207 rs110081798 3 Meat_and_Carcass 110866521 110866525 152332
86 ARS-BFGL-NGS-74948 rs41585925 3 Health 111487002 111487006 137202
87 ARS-BFGL-NGS-74948 rs41585925 3 Health 111487002 111487006 170274
88 ARS-BFGL-NGS-74948 rs41585925 3 Health 111509252 111509256 170275
89 ARS-BFGL-NGS-74948 rs41585925 3 Health 111515878 111515882 170276
90 BTB-01641394 rs42752353 3 Reproduction 111708234 111708238 30008
91 ARS-BFGL-NGS-37809 rs42751504 3 Reproduction 111708234 111708238 30008
92 BTB-01641394 rs42752353 3 Reproduction 111708234 111708238 30244
93 ARS-BFGL-NGS-37809 rs42751504 3 Reproduction 111708234 111708238 30244
94 BTB-01641394 rs42752353 3 Meat_and_Carcass 111708234 111708238 152258
95 ARS-BFGL-NGS-37809 rs42751504 3 Meat_and_Carcass 111708234 111708238 152258
96 BTB-01641394 rs42752353 3 Meat_and_Carcass 111717521 111717525 225527
97 ARS-BFGL-NGS-37809 rs42751504 3 Meat_and_Carcass 111717521 111717525 225527
98 BTB-00594449 rs41764450 15 Production 33066382 33066386 68791
99 BTB-00594449 rs41764450 15 Meat_and_Carcass 33107568 33107572 228743
100 ARS-BFGL-NGS-30515 rs110565206 15 Milk 57892792 57892796 242339
101 ARS-BFGL-NGS-30515 rs110565206 15 Milk 57914040 57914044 248429
102 ARS-BFGL-NGS-30515 rs110565206 15 Milk 57926776 57926780 242396
103 ARS-BFGL-NGS-30515 rs110565206 15 Milk 57926776 57926780 248428
104 ARS-BFGL-NGS-30515 rs110565206 15 Milk 57949776 57949780 242391
105 ARS-BFGL-NGS-41631 rs110121846 29 Production 33039861 33039865 69450
106 ARS-BFGL-NGS-41631 rs110121846 29 Production 33039861 33039865 69451
107 ARS-BFGL-NGS-82859 rs110506037 14 Milk 15895119 15895123 25606
108 ARS-BFGL-NGS-82859 rs110506037 14 Milk 15895119 15895123 26355
109 ARS-BFGL-NGS-82859 rs110506037 14 Meat_and_Carcass 15895119 15895123 36989
110 ARS-BFGL-NGS-82859 rs110506037 14 Milk 15904094 15904098 104606
111 ARS-BFGL-NGS-82859 rs110506037 14 Milk 15905538 15905542 104607
112 ARS-BFGL-NGS-82859 rs110506037 14 Exterior 15929820 15929824 220989
113 ARS-BFGL-NGS-82859 rs110506037 14 Production 15964283 15964287 122755
114 ARS-BFGL-NGS-82859 rs110506037 14 Production 15964283 15964287 123539
115 ARS-BFGL-NGS-82859 rs110506037 14 Production 15965017 15965021 122756
116 ARS-BFGL-NGS-82859 rs110506037 14 Production 15965017 15965021 123540
117 ARS-BFGL-NGS-82859 rs110506037 14 Production 15967515 15967519 122757
118 ARS-BFGL-NGS-82859 rs110506037 14 Production 15967515 15967519 123541
119 ARS-BFGL-NGS-82859 rs110506037 14 Production 15969384 15969388 122758
120 ARS-BFGL-NGS-82859 rs110506037 14 Production 15969384 15969388 123542
121 ARS-BFGL-NGS-82859 rs110506037 14 Production 15972216 15972220 122759
122 ARS-BFGL-NGS-82859 rs110506037 14 Production 15972216 15972220 123543
123 ARS-BFGL-NGS-82859 rs110506037 14 Production 15977822 15977826 122760
124 ARS-BFGL-NGS-82859 rs110506037 14 Production 15977822 15977826 123544
125 ARS-BFGL-NGS-91119 rs109575643 20 Milk 65321359 65321363 176120
126 ARS-BFGL-NGS-91119 rs109575643 20 Milk 65350145 65350149 69962
127 ARS-BFGL-NGS-91119 rs109575643 20 Milk 65352215 65352219 69963
128 ARS-BFGL-NGS-91119 rs109575643 20 Milk 65357974 65357978 69964
129 ARS-BFGL-NGS-91119 rs109575643 20 Milk 65363596 65363600 69626
130 ARS-BFGL-NGS-91119 rs109575643 20 Health 65367232 65367236 167647
131 BTB-00096979 rs43305418 2 Health 41584083 41584087 32371
132 BTB-00096979 rs43305418 2 Milk 41646649 41646653 113699
133 Hapmap48777-BTA-47434 rs41636137 2 Milk 41646649 41646653 113699
134 Hapmap48777-BTA-47434 rs41636137 2 Milk 41668170 41668174 113700
135 Hapmap48777-BTA-47434 rs41636137 2 Meat_and_Carcass 41676143 41676147 154062
136 Hapmap48777-BTA-47434 rs41636137 2 Reproduction 41693862 41693866 29899
137 Hapmap53065-rs29026778 rs29026778 2 Milk 118779183 118779187 200902
138 Hapmap53065-rs29026778 rs29026778 2 Milk 118779985 118779989 202500
139 Hapmap53065-rs29026778 rs29026778 2 Milk 118781186 118781190 202905
140 Hapmap53065-rs29026778 rs29026778 2 Milk 118785252 118785256 201638
141 Hapmap53065-rs29026778 rs29026778 2 Milk 118789209 118789213 202885
142 Hapmap53065-rs29026778 rs29026778 2 Reproduction 118820216 118820220 39599
143 Hapmap53065-rs29026778 rs29026778 2 Reproduction 118820216 118820220 39600
144 Hapmap53065-rs29026778 rs29026778 2 Reproduction 118820216 118820220 39601
145 Hapmap53065-rs29026778 rs29026778 2 Exterior 118820216 118820220 39602
146 Hapmap53065-rs29026778 rs29026778 2 Exterior 118820216 118820220 39603
147 Hapmap53065-rs29026778 rs29026778 2 Milk 118820216 118820220 39604
148 Hapmap53065-rs29026778 rs29026778 2 Production 118820216 118820220 39605
149 Hapmap53065-rs29026778 rs29026778 2 Exterior 118820216 118820220 39606
150 Hapmap53065-rs29026778 rs29026778 2 Milk 118820216 118820220 39607
151 Hapmap53065-rs29026778 rs29026778 2 Production 118820216 118820220 39608
152 Hapmap53065-rs29026778 rs29026778 2 Production 118820216 118820220 39609
153 Hapmap53065-rs29026778 rs29026778 2 Exterior 118820216 118820220 39610
154 Hapmap53065-rs29026778 rs29026778 2 Exterior 118820216 118820220 39611
155 Hapmap53065-rs29026778 rs29026778 2 Production 118820216 118820220 39612
156 Hapmap53065-rs29026778 rs29026778 2 Reproduction 118820216 118820220 39613
157 Hapmap53065-rs29026778 rs29026778 2 Health 118820216 118820220 39614
158 Hapmap53065-rs29026778 rs29026778 2 Exterior 118820216 118820220 39615
159 Hapmap53065-rs29026778 rs29026778 2 Exterior 118820216 118820220 39616
160 Hapmap53065-rs29026778 rs29026778 2 Reproduction 118820216 118820220 125235
161 Hapmap23854-BTC-062412 rs81154019 6 Milk 36141496 36141500 244115
162 Hapmap23854-BTC-062412 rs81154019 6 Milk 36141496 36141500 246435
163 Hapmap23854-BTC-062412 rs81154019 6 Milk 36141496 36141500 250700
164 Hapmap23854-BTC-062412 rs81154019 6 Production 36143407 36143411 190189
165 Hapmap23854-BTC-062412 rs81154019 6 Production 36143929 36143933 187391
166 Hapmap23854-BTC-062412 rs81154019 6 Production 36145023 36145027 183480
167 Hapmap23854-BTC-062412 rs81154019 6 Production 36145023 36145027 190272
168 Hapmap23854-BTC-062412 rs81154019 6 Production 36145254 36145258 182513
169 Hapmap23854-BTC-062412 rs81154019 6 Production 36145254 36145258 186692
170 Hapmap23854-BTC-062412 rs81154019 6 Production 36147416 36147420 187702
171 Hapmap23854-BTC-062412 rs81154019 6 Production 36147705 36147709 186912
172 Hapmap23854-BTC-062412 rs81154019 6 Production 36148373 36148377 182603
173 Hapmap23854-BTC-062412 rs81154019 6 Production 36148373 36148377 188134
174 Hapmap23854-BTC-062412 rs81154019 6 Production 36149039 36149043 186551
175 Hapmap23854-BTC-062412 rs81154019 6 Production 36150874 36150878 183765
176 Hapmap23854-BTC-062412 rs81154019 6 Production 36150874 36150878 186794
177 Hapmap23854-BTC-062412 rs81154019 6 Production 36151323 36151327 183528
178 Hapmap23854-BTC-062412 rs81154019 6 Production 36151323 36151327 187598
179 Hapmap23854-BTC-062412 rs81154019 6 Production 36153011 36153015 188352
180 Hapmap23854-BTC-062412 rs81154019 6 Production 36154453 36154457 188950
181 Hapmap23854-BTC-062412 rs81154019 6 Production 36156604 36156608 187155
182 Hapmap23854-BTC-062412 rs81154019 6 Production 36156892 36156896 183350
183 Hapmap23854-BTC-062412 rs81154019 6 Production 36156892 36156896 187196
184 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 23700
185 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 66352
186 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 66353
187 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 66354
188 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 66355
189 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 67222
190 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 67223
191 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 164068
192 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 164167
193 Hapmap23854-BTC-062412 rs81154019 6 Meat_and_Carcass 36157620 36157624 164343
194 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 164369
195 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 164488
196 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 183351
197 Hapmap23854-BTC-062412 rs81154019 6 Production 36157620 36157624 186777
198 Hapmap23854-BTC-062412 rs81154019 6 Production 36157986 36157990 187308
199 Hapmap23854-BTC-062412 rs81154019 6 Production 36158359 36158363 187062
200 Hapmap23854-BTC-062412 rs81154019 6 Production 36158498 36158502 186952
201 Hapmap23854-BTC-062412 rs81154019 6 Production 36159606 36159610 183352
202 Hapmap23854-BTC-062412 rs81154019 6 Production 36159606 36159610 187046
203 Hapmap23854-BTC-062412 rs81154019 6 Production 36160694 36160698 187096
204 Hapmap23854-BTC-062412 rs81154019 6 Production 36161273 36161277 187575
205 Hapmap23854-BTC-062412 rs81154019 6 Production 36161375 36161379 186498
206 Hapmap23854-BTC-062412 rs81154019 6 Production 36161625 36161629 187580
207 Hapmap23854-BTC-062412 rs81154019 6 Production 36161633 36161637 187885
208 Hapmap23854-BTC-062412 rs81154019 6 Production 36162950 36162954 187283
209 Hapmap23854-BTC-062412 rs81154019 6 Production 36163340 36163344 187023
210 Hapmap23854-BTC-062412 rs81154019 6 Production 36163428 36163432 186547
211 Hapmap23854-BTC-062412 rs81154019 6 Reproduction 36163939 36163943 14703
212 Hapmap23854-BTC-062412 rs81154019 6 Reproduction 36163939 36163943 14707
213 Hapmap23854-BTC-062412 rs81154019 6 Production 36163939 36163943 189833
214 Hapmap23854-BTC-062412 rs81154019 6 Production 36164020 36164024 189834
215 Hapmap23854-BTC-062412 rs81154019 6 Production 36164564 36164568 187124
216 Hapmap23854-BTC-062412 rs81154019 6 Production 36165001 36165005 186965
217 Hapmap23854-BTC-062412 rs81154019 6 Production 36165137 36165141 187629
218 Hapmap23854-BTC-062412 rs81154019 6 Production 36165150 36165154 188219
219 Hapmap23854-BTC-062412 rs81154019 6 Production 36165218 36165222 186478
220 Hapmap23854-BTC-062412 rs81154019 6 Production 36165305 36165309 187300
221 Hapmap23854-BTC-062412 rs81154019 6 Production 36165645 36165649 186485
222 Hapmap23854-BTC-062412 rs81154019 6 Production 36166108 36166112 187795
223 Hapmap23854-BTC-062412 rs81154019 6 Production 36166178 36166182 187257
224 Hapmap23854-BTC-062412 rs81154019 6 Production 36166205 36166209 187129
225 Hapmap23854-BTC-062412 rs81154019 6 Production 36166298 36166302 187368
226 Hapmap23854-BTC-062412 rs81154019 6 Reproduction 36166503 36166507 14704
227 Hapmap23854-BTC-062412 rs81154019 6 Milk 36166503 36166507 105821
228 Hapmap23854-BTC-062412 rs81154019 6 Production 36166503 36166507 189279
229 Hapmap23854-BTC-062412 rs81154019 6 Production 36166791 36166795 189278
230 Hapmap23854-BTC-062412 rs81154019 6 Production 36166795 36166799 182735
231 Hapmap23854-BTC-062412 rs81154019 6 Production 36166795 36166799 189277
232 Hapmap23854-BTC-062412 rs81154019 6 Production 36167325 36167329 183190
233 Hapmap23854-BTC-062412 rs81154019 6 Production 36167325 36167329 186569
234 Hapmap23854-BTC-062412 rs81154019 6 Production 36167828 36167832 183191
235 Hapmap23854-BTC-062412 rs81154019 6 Production 36167828 36167832 188015
236 Hapmap23854-BTC-062412 rs81154019 6 Production 36168124 36168128 183305
237 Hapmap23854-BTC-062412 rs81154019 6 Production 36168124 36168128 186440
238 Hapmap23854-BTC-062412 rs81154019 6 Production 36168592 36168596 182749
239 Hapmap23854-BTC-062412 rs81154019 6 Production 36168592 36168596 186494
240 Hapmap23854-BTC-062412 rs81154019 6 Production 36168720 36168724 182760
241 Hapmap23854-BTC-062412 rs81154019 6 Production 36168720 36168724 186560
242 Hapmap23854-BTC-062412 rs81154019 6 Production 36169712 36169716 187059
243 Hapmap23854-BTC-062412 rs81154019 6 Production 36169792 36169796 186511
244 Hapmap23854-BTC-062412 rs81154019 6 Milk 36170113 36170117 140218
245 Hapmap23854-BTC-062412 rs81154019 6 Production 36170133 36170137 186638
246 Hapmap23854-BTC-062412 rs81154019 6 Production 36170199 36170203 187467
247 Hapmap23854-BTC-062412 rs81154019 6 Production 36171684 36171688 188463
248 Hapmap23854-BTC-062412 rs81154019 6 Production 36172370 36172374 187237
249 Hapmap23854-BTC-062412 rs81154019 6 Milk 36172376 36172380 246431
250 Hapmap23854-BTC-062412 rs81154019 6 Production 36177199 36177203 183152
251 Hapmap23854-BTC-062412 rs81154019 6 Production 36177199 36177203 188011
252 Hapmap23854-BTC-062412 rs81154019 6 Production 36180643 36180647 187727
253 Hapmap23854-BTC-062412 rs81154019 6 Production 36181019 36181023 187465
254 Hapmap23854-BTC-062412 rs81154019 6 Production 36182792 36182796 187002
255 Hapmap23854-BTC-062412 rs81154019 6 Production 36183911 36183915 186509
256 Hapmap23854-BTC-062412 rs81154019 6 Milk 36185332 36185336 250783
257 Hapmap23854-BTC-062412 rs81154019 6 Milk 36186103 36186107 103888
258 Hapmap23854-BTC-062412 rs81154019 6 Milk 36186103 36186107 104752
259 Hapmap23854-BTC-062412 rs81154019 6 Production 36186581 36186585 188641
260 Hapmap23854-BTC-062412 rs81154019 6 Production 36186688 36186692 189503
261 Hapmap23854-BTC-062412 rs81154019 6 Production 36187039 36187043 188875
262 Hapmap23854-BTC-062412 rs81154019 6 Production 36189076 36189080 188768
263 Hapmap23854-BTC-062412 rs81154019 6 Production 36191254 36191258 187381
264 Hapmap23854-BTC-062412 rs81154019 6 Production 36198700 36198704 186480
265 Hapmap23854-BTC-062412 rs81154019 6 Production 36203596 36203600 187794
266 Hapmap23854-BTC-062412 rs81154019 6 Milk 36204034 36204038 138157
267 Hapmap23854-BTC-062412 rs81154019 6 Production 36204034 36204038 186572
268 Hapmap23854-BTC-062412 rs81154019 6 Milk 36205214 36205218 15002
269 Hapmap23854-BTC-062412 rs81154019 6 Production 36206851 36206855 188861
270 Hapmap23854-BTC-062412 rs81154019 6 Production 36210548 36210552 187623
271 Hapmap23854-BTC-062412 rs81154019 6 Production 36211595 36211599 187645
272 Hapmap23854-BTC-062412 rs81154019 6 Production 36212184 36212188 189171
273 Hapmap23854-BTC-062412 rs81154019 6 Production 36212912 36212916 187151
274 Hapmap23854-BTC-062412 rs81154019 6 Production 36216422 36216426 187483
275 Hapmap23854-BTC-062412 rs81154019 6 Milk 36218000 36218004 103890
276 Hapmap23854-BTC-062412 rs81154019 6 Milk 36218000 36218004 104754
277 Hapmap23854-BTC-062412 rs81154019 6 Milk 36219842 36219846 105620
278 Hapmap23854-BTC-062412 rs81154019 6 Production 36219842 36219846 186455
279 Hapmap23854-BTC-062412 rs81154019 6 Production 36221082 36221086 187299
280 Hapmap23854-BTC-062412 rs81154019 6 Production 36221438 36221442 187378
281 Hapmap23854-BTC-062412 rs81154019 6 Production 36225616 36225620 186759
282 Hapmap23854-BTC-062412 rs81154019 6 Production 36226441 36226445 186605
283 Hapmap23854-BTC-062412 rs81154019 6 Production 36226963 36226967 23701
284 Hapmap23854-BTC-062412 rs81154019 6 Production 36226963 36226967 67245
285 Hapmap23854-BTC-062412 rs81154019 6 Production 36226963 36226967 67246
286 Hapmap23854-BTC-062412 rs81154019 6 Production 36226963 36226967 67247
287 Hapmap23854-BTC-062412 rs81154019 6 Milk 36226963 36226967 105509
288 Hapmap23854-BTC-062412 rs81154019 6 Production 36226963 36226967 164069
289 Hapmap23854-BTC-062412 rs81154019 6 Production 36226963 36226967 164370
290 Hapmap23854-BTC-062412 rs81154019 6 Production 36226963 36226967 186841
291 Hapmap23854-BTC-062412 rs81154019 6 Production 36227839 36227843 182676
292 Hapmap23854-BTC-062412 rs81154019 6 Production 36227839 36227843 188422
293 Hapmap23854-BTC-062412 rs81154019 6 Production 36232422 36232426 187967
294 Hapmap23854-BTC-062412 rs81154019 6 Production 36232742 36232746 187367
295 Hapmap23854-BTC-062412 rs81154019 6 Production 36233671 36233675 188839
296 Hapmap47669-BTA-59022 rs41645754 24 Exterior 857726 857730 66121
297 Hapmap47669-BTA-59022 rs41645754 24 Reproduction 885598 885602 106889

QTL type My Image

QTL name by type My Image

9.3.7.1 QTL enrichment on GALLO

#QTL enrichment analysis 
out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]

write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_enrich_qtl_genome_name.csv")

out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_enrich_qtl_genome_type.csv")


#Plots

#Name

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL," - ","CHR",out.enrich_qtl_name$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type

#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL," - ","CHR",out.enrich_qtl_type$CHR,sep="")

#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]

#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X QTL N_QTLs N_QTLs_db Total_annotated_QTLs Total_QTLs_db pvalue adj.pval QTL_type
27 Metabolic body weight 83 4039 292 163224 0.0000000 0.0000000 Production
29 Milk butyric acid content 22 828 292 163224 0.0000000 0.0000000 Milk
30 Milk caproic acid content 18 675 292 163224 0.0000000 0.0000000 Milk
12 Dry matter intake 17 2186 292 163224 0.0000006 0.0000090 Production
2 Aggressive behavior 2 8 292 163224 0.0000887 0.0008719 Exterior
25 Length of productive life 13 2004 292 163224 0.0000815 0.0008719 Production
35 Milk pentadecylic acid content 5 280 292 163224 0.0001640 0.0013823 Milk
33 Milk iron content 4 217 292 163224 0.0006685 0.0049302 Milk
15 Dystocia 2 31 292 163224 0.0014331 0.0090411 Reproduction
16 Fecal larva count 3 125 292 163224 0.0015324 0.0090411 Health
4 Body weight 16 4289 292 163224 0.0050041 0.0268400 Production
14 Duration of inactivity during open field test 1 9 292 163224 0.0159862 0.0785990 Exterior
13 Duration of exploration during novel object test 1 16 292 163224 0.0282437 0.1281828 Exterior
5 Body weight gain 6 1354 292 163224 0.0362258 0.1526660 Production
3 Anti-Müllerian hormone level 1 26 292 163224 0.0454908 0.1789305 Health
46 Polyunsaturated fatty acid content 1 28 292 163224 0.0489034 0.1803314 Meat and Carcass
45 Palmitoleic acid content 1 46 292 163224 0.0790752 0.2744374 Meat and Carcass
42 Muscle sodium content 1 56 292 163224 0.0954231 0.3127758 Meat and Carcass
28 Milk alpha-S1-casein percentage 1 65 292 163224 0.1098888 0.3382449 Milk
43 Myristoleic acid content 1 68 292 163224 0.1146593 0.3382449 Meat and Carcass
39 Milk unglycosylated kappa-casein percentage 7 2351 292 163224 0.1315212 0.3695119 Milk
21 Interdigital hyperplasia 1 93 292 163224 0.1534352 0.4052354 Exterior
41 Multiple birth 1 96 292 163224 0.1579731 0.4052354 Reproduction
19 Gestation length 2 636 292 163224 0.3149170 0.7146195 Reproduction
23 Interval to first estrus after calving 3 1053 292 163224 0.2917668 0.7146195 Reproduction
36 Milk protein percentage 18 8803 292 163224 0.3139794 0.7146195 Milk
8 Calving ease 6 3819 292 163224 0.6804471 0.9778366 Reproduction
10 Conception rate 2 1255 292 163224 0.6577041 0.9778366 Reproduction
17 Feet and leg conformation 1 627 292 163224 0.6752950 0.9778366 Exterior
18 Foot angle 1 672 292 163224 0.7005282 0.9778366 Exterior
22 Interval from first to last insemination 1 445 292 163224 0.5497190 0.9778366 Reproduction
24 Lean meat yield 1 621 292 163224 0.6717742 0.9778366 Meat and Carcass
38 Milk riboflavin content 1 509 292 163224 0.5986072 0.9778366 Milk
48 PTA type 1 627 292 163224 0.6752950 0.9778366 Production
49 Rear leg placement - side view 1 430 292 163224 0.5374280 0.9778366 Exterior
50 Rump width 1 526 292 163224 0.6106790 0.9778366 Production
52 Somatic cell score 2 1122 292 163224 0.5971200 0.9778366 Health
53 Stillbirth 2 1363 292 163224 0.7013705 0.9778366 Reproduction
54 Strength 1 664 292 163224 0.6961897 0.9778366 Exterior
55 Subcutaneous fat thickness 1 331 292 163224 0.4474847 0.9778366 Meat and Carcass
57 Udder attachment 1 655 292 163224 0.6912339 0.9778366 Exterior
58 Udder depth 1 695 292 163224 0.7126605 0.9778366 Exterior
59 Udder height 1 504 292 163224 0.5949862 0.9778366 Exterior
6 Bovine respiratory disease susceptibility 1 789 292 163224 0.7573589 0.9935536 Health
20 Inseminations per conception 1 790 292 163224 0.7577951 0.9935536 Reproduction
1 Age at puberty 1 8222 292 163224 0.9999997 0.9999997 Reproduction
7 Bovine tuberculosis susceptibility 1 1155 292 163224 0.8744991 0.9999997 Health
9 Carcass weight 1 2020 292 163224 0.9737344 0.9999997 Meat and Carcass
11 Connective tissue amount 1 3142 292 163224 0.9965892 0.9999997 Meat and Carcass
26 Marbling score 1 1817 292 163224 0.9620577 0.9999997 Meat and Carcass
31 Milk fat percentage 9 10941 292 163224 0.9979006 0.9999997 Milk
32 Milk fat yield 2 8220 292 163224 0.9999954 0.9999997 Milk
34 Milk kappa-casein percentage 5 4499 292 163224 0.9064431 0.9999997 Milk
37 Milk protein yield 1 3093 292 163224 0.9962701 0.9999997 Milk
40 Milk yield 4 6432 292 163224 0.9970834 0.9999997 Milk
44 Net merit 1 903 292 163224 0.8023701 0.9999997 Production
47 Pregnancy rate 1 944 292 163224 0.8164356 0.9999997 Reproduction
51 Shear force 2 2954 292 163224 0.9692776 0.9999997 Meat and Carcass
56 Tenderness score 4 3483 292 163224 0.8712212 0.9999997 Meat and Carcass

My Image

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X QTL N_QTLs N_QTLs_db Total_annotated_QTLs Total_QTLs_db pvalue adj.pval
1 Exterior 12 9077 292 163224 0.8914062 1
2 Health 8 5889 292 163224 0.8293292 1
3 Meat and Carcass 15 18258 292 163224 0.9998999 1
4 Milk 97 75352 292 163224 0.9999974 1
5 Production 138 19640 292 163224 0.0000000 0
6 Reproduction 22 35008 292 163224 1.0000000 1

My Image

9.3.7.2 BLUPF90+ GPROFILER ON-LINE

From the online version of GPROFILER i got the following results.

Legend My Image

My Image

My Image

9.3.7.3 BLUPF90+ VEP

My Image

rs110079750 My Image

rs109276211 My Image

rs109631116 My Image

rs42029843 My Image

rs110428837 My Image

rs110081798 My Image

rs110160157 My Image

rs110038841 My Image

rs109868537 My Image

rs110565206 My Image

rs42751504 My Image

rs110121846 My Image

rs110935391 My Image

rs109584097 My Image

rs41585925 My Image

rs110506037 My Image

rs109273103 My Image

rs109575643 My Image

rs43305418 My Image

rs43377276 My Image

rs43691687 My Image

rs41764450 My Image

rs42305073 No

rs42462935 No

rs42752353 My Image

rs81154019 No

rs41645754 My Image

rs41566051 My Image

rs41636137 My Image

rs41615935 My Image

rs29026778 My Image

rs29013980 My Image

rs29023486 My Image

rs29027498 My Image

rs29017061 My Image

rs29018741 My Image

rs29013301 My Image

rs29012492 My Image

It is interesting that 3 significant SNPs falled in the same gene CSMD2 on chromosome 3 rs109868537 rs42751504 rs42752353

My Image

My Image

For the gene CNTNAP2 on chromosome 4 also have 2 significant SNPs rs41566051 rs29012492

My Image

9.4 BLUPF90+ WINDOWS

9.4.1 Running renf90_ssGWAS2_SNPeff_w.par for GWAS (WINDOWS)

The parameter card bellow we are going to run using the software postGSf90:

renf90_ssGWAS2_SNPeff_W_10.par

# BLUPF90 parameter file created by RENUMF90
DATAFILE
renf90.dat
NUMBER_OF_TRAITS
           1
NUMBER_OF_EFFECTS
           3
OBSERVATION(S)
    1
WEIGHT(S)
 
EFFECTS: POSITIONS_IN_DATAFILE NUMBER_OF_LEVELS TYPE_OF_EFFECT[EFFECT NESTED]
 2         4 cross 
 3        22 cross 
 4      3724 cross 
RANDOM_RESIDUAL VALUES
   77182.    
 RANDOM_GROUP
     3
 RANDOM_TYPE
 add_an_upginb
 FILE
renadd03.ped                                                                    
(CO)VARIANCES
   28212.    
OPTION SNP_file bruno_gntps_iid_71_clean
OPTION origID
OPTION no_quality_control
OPTION readGInverse
OPTION readA22Inverse
OPTION map_file snpmap.txt_clean
OPTION snp_p_value
OPTION Manhattan_plot_R
OPTION Manhattan_plot
OPTION SNP_moving_average 10
OPTION windows_variance 10 
The parameter file above will generate the following files:
  • snp_sol
    • column 1: trait
    • column 2: effect
    • column 3: SNP
    • column 4: Chromosome
    • column 5: Position
    • column 6: SNP solution
    • column 7: weight
    • column 8: variance explained by n adjacents SNP (if OPTION windows_variance is used).
    • column 9: variance of the SNP solution (used to compute the p-value) (if OPTION snp_p_value is used)
  • snp_pred
    • contains allele frequencies + SNP effects
  • chrsnpvar
    • column 1: trait
    • column 2: effect
    • column 3: variance explained by n adjacents SNP
    • column 4: SNP
    • column 5: Chromosome
    • column 6: Position
  • chrsnp_pval
    • column 1: trait
    • column 2: effect
    • column 3: -log10(p-value)
    • column 4: SNP
    • column 5: Chromosome
    • column 6: Position in bp
  • chrsnp
    • column 1: trait
    • column 2: effect
    • column 3: values of SNP effects to use in Manhattan plots → [abs(SNP_i)/SD(SNP)]
    • column 4: SNP
    • column 5: Chromosome
    • column 6: Position
  • windows_segments
    • column 1: label
    • column 2: window size (number of SNP)
    • column 3: Start SNP number for the window
    • column 4: End SNP number for the window
    • column 5: identification of window: (ChrNumber)’_’(startPositionMBP)
    • column 6: Start (ChrNumber)’_’(Position) for the window
    • column 7: End (ChrNumber)’_’(Position) for the window
  • windows_variance
    • column 1: trait
    • column 2: effect
    • column 3: Start SNP number or SNP name for the window
    • column 4: End SNP number or SNP name for the window
    • column 5: window size (number of SNP)
    • column 6: Start (ChrNumber)’_’(Position) for the window
    • column 7: End (ChrNumber)’_’(Position) for the window
    • column 8: identification of window: (ChrNumber)’_’(startPositionMBP)
    • column 9: variance explained by n adjacents SNP
  • Vft1e3.R
  • Sft1e3.R
  • Pft1e3.R

Bellow we can see the SNPs that explain more than 0.5% of Genetic Variance

w_var <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/chrsnpvar", header = F)
w_var <- filter(w_var, V3 > 0.5)
colnames(w_var) <- c("V1", "V2", "Var", "SNP", "CHR", "BP")
snp_map <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/snpmap.txt_clean", header = T)

# Fazer o merge baseado em duas condições: CHR e POS
merged_data <- merge(w_var, snp_map, by.x = c("CHR", "BP"), by.y = c("CHR", "POS"), all.x = TRUE)
w_var <- merged_data[,c("CHR", "BP", "Var", "SNP_ID")]


rsid <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/SNPchimp_result_3859303481.tsv", header = T)


merged <- merge(rsid, w_var, by.x ="SNP_name", by.y ="SNP_ID")

colnames(merged)

merged <- merged[,c("SNP_name", "rs", "CHR", "BP", "Var")]

colnames(merged) <- c("SNP_name", "rsID", "CHR", "BP", "Var")

write.csv(merged, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_05.csv")
X SNP_name rsID CHR BP Var
1 ARS-BFGL-BAC-28665 rs111010562 24 28487771 0.6899732
2 ARS-BFGL-BAC-35548 rs110100182 2 115665427 1.2851806
3 ARS-BFGL-BAC-7444 rs110491621 13 18558540 0.6630789
4 ARS-BFGL-NGS-103753 rs110842922 2 115821065 1.1689686
5 ARS-BFGL-NGS-107330 rs109766798 2 116018639 0.5958551
6 ARS-BFGL-NGS-25298 rs109868537 3 111772736 1.2101267
7 ARS-BFGL-NGS-2713 rs41761360 15 34054485 0.5549892
8 ARS-BFGL-NGS-30337 rs110485060 2 115730530 1.3488469
9 ARS-BFGL-NGS-3276 rs110634531 20 12087403 0.5722138
10 ARS-BFGL-NGS-37809 rs42751504 3 111751663 1.0607739
11 ARS-BFGL-NGS-43721 rs108974471 2 115986085 0.8178265
12 ARS-BFGL-NGS-44131 rs110100483 3 111806406 1.0252690
13 ARS-BFGL-NGS-5141 rs110705087 24 28417928 0.5945940
14 ARS-BFGL-NGS-5976 rs41763278 15 34144843 0.5490537
15 ARS-BFGL-NGS-6202 rs110385521 3 111833768 1.1671762
16 ARS-BFGL-NGS-78615 rs110959523 20 12111883 0.5689079
17 ARS-BFGL-NGS-85333 rs110742206 3 111933069 0.6077153
18 ARS-BFGL-NGS-97849 rs110553601 3 111965305 0.7547885
19 ARS-BFGL-NGS-98724 rs109709275 15 34109962 0.6252263
20 BTA-25900-no-rs rs41575397 13 18509812 0.6364128
21 BTA-49096-no-rs rs41578131 2 115695003 1.6071883
22 BTA-73915-no-rs rs41648979 5 6312610 0.5103844
23 BTA-91816-no-rs rs41596771 15 11100934 0.9739683
24 BTB-01421844 rs42544667 15 11144666 0.7131750
25 BTB-01421892 rs42544714 15 11185546 0.6463689
26 BTB-01421934 rs42545356 15 11207429 0.8898567
27 BTB-01422008 rs42545430 15 11236303 0.6832306
28 BTB-01434227 rs42557533 10 50554831 0.6660810
29 BTB-01485274 rs42609685 24 28540641 0.8983644
30 BTB-01608944 rs42723390 15 10974997 1.1206945
31 BTB-01641394 rs42752353 3 111730561 0.9480214
32 BTB-01646599 rs42761380 24 28604672 0.5894579
33 BTB-01813405 rs42924913 15 10879514 0.5556342
34 BTB-01830390 rs42938737 15 10936002 1.0480277
35 BTB-01948148 rs43056622 3 111677167 0.7467347
36 BTB-02063964 rs43172105 15 10906064 0.8788211
37 Hapmap41888-BTA-49091 rs41645223 2 115622067 0.8268392
38 Hapmap42062-BTA-109789 rs41621207 3 111708236 1.2415855
39 Hapmap49833-BTA-103929 rs41603335 13 18386942 0.5282867
40 Hapmap50266-BTA-13664 rs29018622 13 18266073 0.6006650
41 Hapmap54770-rs29009608 rs29009608 2 115875702 0.7398417
42 Hapmap54981-rs29019846 rs29019846 24 28516684 0.7747524
43 Hapmap58887-rs29013502 rs29013502 24 28570245 0.7619213

Bellow we can see the SNPs that explain more than 0.1% of Genetic Variance

w_var <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/chrsnpvar", header = F)
w_var <- filter(w_var, V3 > 0.1)
colnames(w_var) <- c("V1", "V2", "Var", "SNP", "CHR", "BP")
snp_map <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/snpmap.txt_clean", header = T)

# Fazer o merge baseado em duas condições: CHR e POS
merged_data <- merge(w_var, snp_map, by.x = c("CHR", "BP"), by.y = c("CHR", "POS"), all.x = TRUE)
w_var <- merged_data[,c("CHR", "BP", "SNP_ID", "Var")]


write.csv(w_var, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_01.csv")

ps: we don’t have rsID for the SNPs which explain more than 0.1% because there are 1,089 different SNPs and SNPchimp can’t deal with this ammount of data.

X CHR BP SNP_ID Var
1 1 101633865 BTB-00807137 0.1011903
2 1 101656030 BTB-00807095 0.2310848
3 1 101729648 BTA-51653-no-rs 0.1240284
4 1 101758062 BTB-00047321 0.1695202
5 1 101780022 ARS-BFGL-NGS-35839 0.1294318
6 1 101894473 ARS-BFGL-NGS-11628 0.1938700
7 1 101978904 BTA-46150-no-rs 0.1424979
8 1 102023440 ARS-BFGL-NGS-54096 0.1921858
9 1 102050836 ARS-BFGL-BAC-4585 0.1247982
10 1 103234850 BTB-00046247 0.1158039
11 1 103306203 BTB-02007023 0.1232947
12 1 103339600 Hapmap42893-BTA-27908 0.1289964
13 1 103405790 BTB-01831301 0.1173550
14 1 103442322 BTB-01579680 0.1228151
15 1 103465675 BTB-01579733 0.1250533
16 1 129991566 ARS-BFGL-NGS-29033 0.1062039
17 1 130055180 ARS-BFGL-NGS-44711 0.1817209
18 1 130171002 ARS-BFGL-NGS-6392 0.2399317
19 1 130204834 ARS-BFGL-NGS-56582 0.3492380
20 1 130225374 BTB-00058404 0.3332314
21 1 130269306 ARS-BFGL-NGS-117553 0.3155001
22 1 130333549 BTA-53036-no-rs 0.2752238
23 1 130363507 BTA-53040-no-rs 0.3226769
24 1 130404213 ARS-BFGL-BAC-5990 0.2582974
25 1 130441749 ARS-BFGL-NGS-111760 0.2461135
26 1 130473510 BTB-00058659 0.1743536
27 1 141713562 ARS-BFGL-NGS-114130 0.1027550
28 1 141793240 ARS-BFGL-NGS-31728 0.1038903
29 1 62768510 Hapmap49510-BTA-17452 0.1013706
30 1 7164618 BTA-44295-no-rs 0.1006185
31 1 7260253 BTA-45181-no-rs 0.1202808
32 1 93906113 BTB-01087150 0.1025507
33 1 93981570 Hapmap42095-BTA-120006 0.1232221
34 1 94035144 ARS-BFGL-NGS-14083 0.1086710
35 1 94088505 ARS-BFGL-NGS-106939 0.1432500
36 1 94134092 BTB-01086885 0.1064178
37 1 96716076 ARS-BFGL-NGS-4387 0.1049353
38 1 96747618 ARS-BFGL-NGS-96411 0.1863716
39 1 96820888 Hapmap42551-BTA-43327 0.1504778
40 1 96856897 ARS-BFGL-NGS-112806 0.1609532
41 1 96883533 BTA-43335-no-rs 0.1132002
42 1 96959413 ARS-BFGL-NGS-94323 0.1345075
43 1 97117901 BTA-43340-no-rs 0.1215449
44 10 14228843 ARS-BFGL-NGS-115059 0.1091091
45 10 14284093 ARS-BFGL-NGS-87702 0.1259792
46 10 15898865 ARS-BFGL-NGS-44234 0.1285237
47 10 15955819 ARS-BFGL-NGS-118236 0.2088687
48 10 15994647 Hapmap47826-BTA-107963 0.2763055
49 10 16137379 ARS-BFGL-BAC-14174 0.2618530
50 10 16160207 ARS-BFGL-NGS-5722 0.3713583
51 10 16182204 ARS-BFGL-NGS-113265 0.4067019
52 10 16211659 ARS-BFGL-NGS-16604 0.2890236
53 10 16346333 ARS-BFGL-NGS-87552 0.2102355
54 10 16434398 BTA-83414-no-rs 0.1754463
55 10 16454781 ARS-BFGL-NGS-64755 0.1610253
56 10 16482947 ARS-BFGL-NGS-43079 0.1010898
57 10 20629073 ARS-BFGL-NGS-33553 0.1191831
58 10 20746648 ARS-BFGL-NGS-57195 0.1243243
59 10 20776707 ARS-BFGL-BAC-10960 0.1315945
60 10 20803826 ARS-BFGL-NGS-42236 0.1818727
61 10 20834705 ARS-BFGL-NGS-44623 0.1702434
62 10 20860225 ARS-BFGL-NGS-110110 0.1283561
63 10 27185490 ARS-BFGL-BAC-11612 0.1467179
64 10 27219761 BTB-01402638 0.1192233
65 10 27257251 UA-IFASA-4465 0.1662910
66 10 27290407 ARS-BFGL-NGS-117948 0.1961370
67 10 27367801 BTB-01697310 0.1974927
68 10 27397646 Hapmap47512-BTA-114443 0.1828933
69 10 27420541 BTB-01402397 0.2176616
70 10 27547632 BTA-23031-no-rs 0.1080350
71 10 28233868 BTB-00417448 0.1652340
72 10 28382493 BTB-00414548 0.1388985
73 10 28417433 BTB-00414590 0.1441956
74 10 28450074 BTB-00414664 0.1264555
75 10 28490691 ARS-BFGL-NGS-3160 0.1201121
76 10 29764271 ARS-BFGL-NGS-46096 0.1067981
77 10 32278745 BTB-00416033 0.1549731
78 10 32303809 BTB-00416055 0.1030607
79 10 32324758 BTB-00416071 0.2002106
80 10 32400485 UA-IFASA-8860 0.1293646
81 10 32433557 BTB-00416188 0.1450198
82 10 32475837 BTB-00416290 0.1245701
83 10 32501730 BTB-00416386 0.1433081
84 10 32581893 BTA-62321-no-rs 0.1447698
85 10 41626921 BTA-96964-no-rs 0.1436904
86 10 41725845 BTA-67183-no-rs 0.1162669
87 10 41823523 Hapmap39501-BTA-67181 0.1439756
88 10 49767155 Hapmap43913-BTA-70188 0.1466788
89 10 49796175 ARS-BFGL-NGS-86147 0.1799150
90 10 49829868 BTB-00426818 0.1527360
91 10 49858694 ARS-BFGL-NGS-88023 0.1496056
92 10 49882556 ARS-BFGL-NGS-70374 0.1590110
93 10 50020863 BTB-00427118 0.1383846
94 10 50119457 BTB-00427217 0.1730071
95 10 50147936 BTB-00427152 0.1150314
96 10 50316929 BTA-70195-no-rs 0.1307258
97 10 50337554 BTB-02009817 0.2964468
98 10 50375243 Hapmap50870-BTA-111277 0.3896697
99 10 50392283 Hapmap41480-BTA-20737 0.2095723
100 10 50415615 BTB-01780583 0.1971266
101 10 50467891 ARS-BFGL-NGS-15394 0.2950876
102 10 50532545 BTA-92902-no-rs 0.4997714
103 10 50554831 BTB-01434227 0.6660810
104 10 50608000 Hapmap51989-BTA-92903 0.4718108
105 10 50632188 BTB-01434144 0.3879825
106 10 50673845 Hapmap54762-rs29012389 0.3575426
107 10 50727713 ARS-BFGL-NGS-8836 0.3454069
108 10 50804429 Hapmap45147-BTA-106533 0.1851935
109 10 50856455 ARS-BFGL-NGS-11399 0.2112604
110 10 50881434 ARS-BFGL-NGS-5470 0.1694916
111 10 50975312 BTB-00428084 0.1242463
112 10 80202103 BTB-00437473 0.1243366
113 10 80223687 ARS-BFGL-NGS-3980 0.2186830
114 10 80258289 UA-IFASA-4740 0.1468626
115 10 80402480 ARS-BFGL-NGS-59601 0.1736052
116 10 80433411 ARS-BFGL-BAC-11007 0.1234922
117 10 80464095 ARS-BFGL-NGS-6116 0.1566501
118 11 12952122 BTB-00460506 0.1061990
119 11 12975419 Hapmap48102-BTA-85468 0.1348135
120 11 13041787 Hapmap52124-rs29020943 0.1585891
121 11 13094818 ARS-BFGL-NGS-20431 0.1428201
122 11 13114842 ARS-BFGL-NGS-2493 0.1626706
123 11 13135356 ARS-BFGL-BAC-14209 0.1306841
124 11 16000971 UA-IFASA-2426 0.1052530
125 11 16110966 BTB-00462075 0.1259478
126 11 16135406 BTB-00461834 0.1063396
127 11 16174031 Hapmap29803-BTA-126260 0.1156433
128 11 16210947 BTB-00461875 0.1042706
129 11 16242950 BTB-00461926 0.1030392
130 11 25467022 ARS-BFGL-NGS-29851 0.1278897
131 11 25630059 BTB-00469321 0.1106776
132 11 25660041 Hapmap42357-BTA-89853 0.1031512
133 11 25760288 ARS-BFGL-NGS-5488 0.1083397
134 11 27648326 ARS-BFGL-NGS-24935 0.1028696
135 11 27678911 BTB-00470332 0.1348225
136 11 27709498 Hapmap58395-rs29019857 0.1645872
137 11 27746425 ARS-BFGL-NGS-101857 0.1297567
138 11 27830023 Hapmap48940-BTA-90883 0.1481492
139 11 28054041 ARS-BFGL-NGS-82906 0.1358081
140 11 29333199 ARS-BFGL-NGS-103581 0.1181737
141 11 29365973 ARS-BFGL-NGS-58445 0.1288754
142 11 29389110 ARS-BFGL-NGS-114530 0.1088233
143 11 29415659 ARS-BFGL-NGS-112640 0.1255939
144 11 29454559 ARS-BFGL-NGS-14040 0.1267679
145 11 29483268 ARS-BFGL-NGS-14449 0.1393408
146 11 29523821 ARS-BFGL-NGS-100266 0.1150293
147 11 58356755 BTA-34561-no-rs 0.1086553
148 11 58383311 BTA-33625-no-rs 0.1036073
149 11 58497959 ARS-BFGL-BAC-11747 0.1017918
150 11 83985738 ARS-BFGL-BAC-7319 0.1051094
151 11 84678345 Hapmap40930-BTA-26841 0.1016702
152 11 84738147 Hapmap25343-BTA-163074 0.1223724
153 11 84820376 BTA-26840-no-rs 0.1085350
154 11 85247389 ARS-BFGL-NGS-37939 0.1346684
155 11 85302066 ARS-BFGL-NGS-107825 0.1411083
156 11 85322845 Hapmap43168-BTA-119307 0.2008925
157 11 85359517 ARS-BFGL-NGS-40301 0.3055185
158 11 85398026 ARS-BFGL-NGS-110235 0.2711184
159 11 85606528 Hapmap57048-rs29017217 0.2491385
160 11 85771451 ARS-BFGL-NGS-111563 0.3654883
161 11 85810220 Hapmap39304-BTA-109342 0.2888586
162 11 85937536 Hapmap38876-BTA-109334 0.3037616
163 11 85972950 BTA-120876-no-rs 0.1692578
164 11 86081075 ARS-BFGL-NGS-14236 0.1476002
165 11 87104511 ARS-BFGL-BAC-7337 0.1237828
166 11 87194574 ARS-BFGL-NGS-38084 0.1577790
167 11 87223121 Hapmap52090-rs29022201 0.1741682
168 11 87279961 BTA-110370-no-rs 0.1707878
169 11 87301374 ARS-BFGL-NGS-114578 0.2879943
170 11 87343394 ARS-BFGL-NGS-115698 0.1751316
171 11 87427230 ARS-BFGL-NGS-89483 0.1791455
172 11 87450947 ARS-BFGL-NGS-77445 0.1715604
173 11 87477968 ARS-BFGL-NGS-102660 0.1509007
174 11 9212734 Hapmap40102-BTA-110410 0.1001916
175 11 9303545 ARS-BFGL-NGS-57976 0.1390568
176 11 9345296 ARS-BFGL-NGS-83673 0.1092555
177 11 95837864 Hapmap24798-BTA-127049 0.1160667
178 11 95985524 Hapmap40206-BTA-120879 0.2015491
179 11 96024175 ARS-BFGL-NGS-72642 0.2154334
180 11 96074398 Hapmap47514-BTA-115564 0.2903548
181 11 96235663 ARS-BFGL-NGS-101698 0.3932618
182 11 96285921 ARS-BFGL-NGS-111672 0.3447816
183 11 96322228 ARS-BFGL-NGS-43490 0.3858722
184 11 96454896 Hapmap60189-rs29017117 0.2919202
185 11 96479338 ARS-BFGL-NGS-50500 0.2485149
186 11 96609577 Hapmap51470-BTA-57893 0.1719578
187 11 96699491 Hapmap51471-BTA-57896 0.1120564
188 12 10779144 BTB-01100174 0.1653103
189 12 10830532 Hapmap35046-BES11_Contig381_736 0.2531744
190 12 10851132 ARS-BFGL-NGS-21526 0.2305728
191 12 10967287 Hapmap48641-BTA-119400 0.2371592
192 12 11150596 ARS-BFGL-NGS-43211 0.3360266
193 12 11193032 ARS-BFGL-NGS-87459 0.2200374
194 12 11324144 ARS-BFGL-NGS-71187 0.1566248
195 12 11385069 BTB-00486553 0.1306997
196 12 11414286 BTB-00487004 0.1364686
197 12 14005588 BTB-01296488 0.1154279
198 12 14056621 Hapmap49074-BTA-19146 0.1209334
199 12 14083373 BTB-01475864 0.1482084
200 12 14109902 BTB-01296573 0.1204048
201 12 14156556 ARS-BFGL-NGS-6072 0.1644307
202 12 14186758 BTB-01296818 0.1135065
203 12 56021401 Hapmap51019-BTA-65454 0.1058319
204 12 56034163 ARS-BFGL-NGS-104509 0.1206410
205 12 56206814 BTB-01834845 0.1078660
206 12 56276159 Hapmap43219-BTA-26954 0.1122969
207 13 17967955 Hapmap51587-BTA-34220 0.1319741
208 13 18001601 ARS-BFGL-NGS-31365 0.1763165
209 13 18167455 Hapmap39556-BTA-34210 0.2841578
210 13 18189413 ARS-BFGL-NGS-30447 0.4135777
211 13 18266073 Hapmap50266-BTA-13664 0.6006650
212 13 18386942 Hapmap49833-BTA-103929 0.5282867
213 13 18489670 ARS-BFGL-NGS-21967 0.4937232
214 13 18509812 BTA-25900-no-rs 0.6364128
215 13 18558540 ARS-BFGL-BAC-7444 0.6630789
216 13 18626070 ARS-BFGL-NGS-116274 0.4947973
217 13 18676544 BTA-31832-no-rs 0.3995710
218 13 18720587 Hapmap43233-BTA-31838 0.2473578
219 13 18755905 BTA-10553-rs29016106 0.1566573
220 13 18792716 ARS-BFGL-NGS-72609 0.1333990
221 13 18822522 ARS-BFGL-NGS-13518 0.1014681
222 13 18870958 ARS-BFGL-NGS-68568 0.1334652
223 13 18892498 ARS-BFGL-NGS-109707 0.1530972
224 13 23248667 Hapmap39219-BTA-31890 0.1386580
225 13 23543854 ARS-BFGL-NGS-112203 0.1473551
226 13 23590146 ARS-BFGL-NGS-101509 0.1353489
227 13 23647519 ARS-BFGL-NGS-99560 0.1323049
228 13 23807096 ARS-BFGL-NGS-80617 0.1505820
229 13 23896954 ARS-BFGL-NGS-14188 0.1365098
230 13 23925448 ARS-BFGL-NGS-91120 0.1240624
231 13 23993075 ARS-BFGL-NGS-31326 0.1228236
232 13 26101077 Hapmap39018-BTA-31978 0.1238798
233 13 58794363 ARS-BFGL-BAC-13190 0.1098366
234 13 59661039 BTB-00534445 0.1336180
235 13 59704623 Hapmap50319-BTA-33109 0.1403262
236 13 59728737 ARS-BFGL-NGS-103807 0.1193523
237 13 59762662 BTB-00534589 0.1499035
238 13 59828052 ARS-BFGL-NGS-108965 0.1342105
239 13 59901998 Hapmap50919-BTA-23085 0.1171896
240 13 59930801 ARS-BFGL-NGS-35771 0.1002085
241 13 75354084 ARS-BFGL-NGS-31974 0.1064442
242 14 12249541 Hapmap23784-BTC-010226 0.1417963
243 14 12811226 UA-IFASA-7429 0.1023518
244 14 12832792 Hapmap56398-rs29010937 0.1966127
245 14 12882729 UA-IFASA-7696 0.3361687
246 14 12970779 Hapmap41845-BTA-35927 0.3373003
247 14 13043711 Hapmap47521-BTA-120525 0.2765382
248 14 13104401 UA-IFASA-8537 0.2641441
249 14 13357962 ARS-BFGL-NGS-104239 0.2672164
250 14 13400490 ARS-BFGL-NGS-35935 0.2016124
251 14 13975863 ARS-BFGL-NGS-19893 0.2394582
252 14 14008185 Hapmap58424-rs29021188 0.2315530
253 14 14041208 BTB-01109852 0.1868193
254 14 14072969 BTB-01109980 0.1048374
255 14 15929822 ARS-BFGL-NGS-82859 0.1135950
256 14 15956824 ARS-BFGL-BAC-10591 0.2270443
257 14 15998933 ARS-BFGL-BAC-14014 0.2031215
258 14 16025038 Hapmap38314-BTA-42171 0.2014397
259 14 16083548 Hapmap60650-rs29021886 0.2162313
260 14 16161546 Hapmap47609-BTA-42173 0.1977502
261 14 16242981 ARS-BFGL-NGS-30433 0.1730677
262 14 16283570 BTA-35990-no-rs 0.1514607
263 14 16409637 BTB-00553442 0.1669021
264 14 19339380 ARS-BFGL-NGS-102399 0.1488057
265 14 25923373 ARS-BFGL-BAC-1302 0.1040812
266 14 64678934 ARS-BFGL-NGS-28894 0.1167324
267 14 64708627 Hapmap49582-BTA-35282 0.2010022
268 14 64752348 Hapmap39171-BTA-35277 0.2158376
269 14 64848878 UA-IFASA-5830 0.1187073
270 14 64929189 ARS-BFGL-BAC-24806 0.1224654
271 14 65078173 ARS-BFGL-NGS-16512 0.1095411
272 14 65243234 ARS-BFGL-NGS-24670 0.1328391
273 14 65391553 ARS-BFGL-NGS-115837 0.1223244
274 14 65761220 ARS-BFGL-NGS-7028 0.1026649
275 14 65824361 UA-IFASA-8451 0.1099946
276 14 66158833 BTA-07375-no-rs 0.1514500
277 14 66258598 ARS-BFGL-NGS-11838 0.1136638
278 14 66308321 ARS-BFGL-NGS-33213 0.1342321
279 14 66350939 ARS-BFGL-NGS-59595 0.1038174
280 14 66525315 ARS-BFGL-NGS-3386 0.1005279
281 14 69322493 ARS-BFGL-NGS-10628 0.1153971
282 14 69343258 Hapmap42350-BTA-88359 0.1058892
283 14 70964025 UA-IFASA-7523 0.1075925
284 15 10740697 BTB-01538650 0.1416967
285 15 10827424 ARS-BFGL-BAC-2191 0.3020577
286 15 10879514 BTB-01813405 0.5556342
287 15 10906064 BTB-02063964 0.8788211
288 15 10936002 BTB-01830390 1.0480277
289 15 10974997 BTB-01608944 1.1206945
290 15 11100934 BTA-91816-no-rs 0.9739683
291 15 11144666 BTB-01421844 0.7131750
292 15 11185546 BTB-01421892 0.6463689
293 15 11207429 BTB-01421934 0.8898567
294 15 11236303 BTB-01422008 0.6832306
295 15 11310260 BTA-91820-no-rs 0.4420277
296 15 11451408 Hapmap40037-BTA-100798 0.2263849
297 15 11816949 ARS-BFGL-NGS-4520 0.1303515
298 15 33804402 Hapmap57840-rs29014510 0.1571340
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1015 7 72621717 Hapmap48186-BTA-112617 0.2037375
1016 7 72657448 ARS-BFGL-NGS-37793 0.1674678
1017 7 72693116 ARS-BFGL-NGS-102365 0.1365016
1018 7 72828445 ARS-BFGL-NGS-107513 0.1127419
1019 7 72852260 Hapmap48995-BTA-103787 0.1183161
1020 7 73473535 Hapmap55033-rs29016821 0.1210329
1021 7 73507133 BTB-00319733 0.1520350
1022 7 73539906 BTB-00319743 0.1352916
1023 7 73574389 BTB-00319755 0.1163384
1024 7 73605674 BTB-00319838 0.1194297
1025 7 73607632 BTB-00319857 0.1257561
1026 7 73637074 Hapmap34667-BES3_Contig253_417 0.1501762
1027 7 90688919 ARS-BFGL-NGS-101886 0.1170778
1028 7 97603739 ARS-BFGL-NGS-42763 0.1486863
1029 7 97813968 BTB-00327062 0.1248129
1030 7 97980945 ARS-BFGL-NGS-96074 0.1293739
1031 8 25725277 BTB-01051883 0.1129557
1032 8 25753902 Hapmap58082-rs29010933 0.1830822
1033 8 25802651 BTB-01051794 0.2412612
1034 8 25830738 Hapmap49881-BTA-120681 0.1891495
1035 8 25858677 BTA-27527-no-rs 0.1284104
1036 8 36246265 ARS-BFGL-NGS-52657 0.1099826
1037 8 55004187 BTB-01820765 0.1480942
1038 8 55190206 Hapmap54208-rs29015846 0.1716331
1039 8 55218811 Hapmap54541-rs29014049 0.2549722
1040 8 55298755 BTA-107975-no-rs 0.1676213
1041 8 55323946 BTB-01616745 0.1450458
1042 8 55376512 ARS-BFGL-NGS-76732 0.1450588
1043 8 55433879 BTB-02017947 0.1047509
1044 8 65038546 ARS-BFGL-NGS-15846 0.1267777
1045 8 65108782 ARS-BFGL-NGS-111488 0.1127684
1046 8 65148173 BTB-00950285 0.1042636
1047 8 70893052 Hapmap39948-BTA-81787 0.1076592
1048 9 10112014 ARS-BFGL-NGS-57285 0.2085905
1049 9 10139748 ARS-BFGL-NGS-37889 0.1640947
1050 9 10221793 ARS-BFGL-NGS-93995 0.1460170
1051 9 32128416 BTB-00386288 0.1029364
1052 9 50084516 BTA-83709-no-rs 0.1075781
1053 9 50127680 UA-IFASA-4980 0.1619502
1054 9 50156167 BTB-00392462 0.1928335
1055 9 50193250 Hapmap49336-BTA-83713 0.1790577
1056 9 50223504 BTB-00392496 0.2264040
1057 9 50246135 Hapmap44146-BTA-83959 0.1558062
1058 9 50288536 BTB-00397090 0.2264369
1059 9 50316056 BTB-00397064 0.1677944
1060 9 50453292 ARS-BFGL-NGS-101365 0.1100639
1061 9 76708517 ARS-BFGL-NGS-116465 0.1330889
1062 9 76804356 ARS-BFGL-NGS-10386 0.1289856
1063 9 76843457 BTA-16148-no-rs 0.1179627
1064 9 87454872 BTB-02079832 0.1128078
1065 9 87701436 Hapmap58334-rs29012728 0.1102492
1066 9 87737922 ARS-BFGL-NGS-28183 0.1508794
1067 9 87761731 BTB-00865957 0.1274192
1068 9 87784500 BTA-56201-no-rs 0.1827472
1069 9 87813977 ARS-BFGL-NGS-6332 0.1002053
1070 9 87859682 Hapmap43079-BTA-84708 0.1233976
1071 9 88080504 Hapmap50128-BTA-84697 0.1210156
1072 9 88113326 ARS-BFGL-NGS-92851 0.1544752
1073 9 88160188 Hapmap58876-ss46526986 0.2696324
1074 9 88210465 BTB-00405541 0.2575876
1075 9 88254314 BTB-00405487 0.2377827
1076 9 88276948 ARS-BFGL-NGS-18187 0.2077461
1077 9 88414100 ARS-BFGL-NGS-97743 0.2119306
1078 9 88532524 ARS-BFGL-NGS-55625 0.1704414
1079 9 88561108 ARS-BFGL-NGS-66371 0.1986769
1080 9 88598336 BTB-00404975 0.1651226
1081 9 88636634 BTB-00404912 0.1047741
1082 9 9212931 ARS-BFGL-NGS-77146 0.1113366
1083 9 9302225 ARS-BFGL-NGS-100620 0.1757817
1084 9 9339314 Hapmap57776-rs29013550 0.1616223
1085 9 9595607 ARS-BFGL-NGS-85616 0.2346046
1086 9 96291120 ARS-BFGL-NGS-119644 0.1188891
1087 9 9643273 ARS-BFGL-NGS-5339 0.2329211
1088 9 9907258 Hapmap41153-BTA-110528 0.2362673
1089 9 9952764 ARS-BFGL-NGS-15799 0.2181269

9.4.1.1 Manhattan Plots for BLUPF90 Windows ssGWAS

# Set working directory
setwd("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10")
getwd()

# Read and process data
yyy1 = read.table("chrsnpvar")
yyy  = yyy1[order(yyy1$V4),]
zzz  = yyy[ which(yyy$V1==1 & yyy$V2==3), ]
n    = nrow(zzz)
y    = zzz[,4]
x    = zzz[,3]
chr1 = zzz[,5]
chr  = NULL
pos  = NULL
for (i in unique(yyy$V5)) {
  zz     = yyy[yyy$V5==i,]
  key    = zz$V4 
  medio  = round(nrow(zz)/2,0)
  z      = key[medio]
  pos    = c(pos,z)
}

# Assign colors for chromosomes
chrn       = unique(yyy$V5)
one        = which(chr1%%4==0) 
two        = which(chr1%%4==1) 
three      = which(chr1%%4==2) 
four       = which(chr1%%4==3) 
chr[one]   = "darkgoldenrod"
chr[two]   = "darkorchid"
chr[three] = "blue"
chr[four]  = "forestgreen"

# Generate Manhattan plot and save to PNG
png(file = "Vft1e3_manplot_with_thresholds.png", 
    width = 20000, height = 10000, res = 600) # Configure width, height, and resolution

# Set plot parameters
par(mfrow = c(1, 1), family = "sans", cex = 1.5, font = 2, mar = c(5, 5, 4, 2))

# Create Manhattan plot
plot(y, x, xaxt = "n", main = "Manhattan Plot SNP Variance explained by 10 adjacents SNP window", 
     xlab = "", ylab = "% variance expl", pch = 20, 
     xlim = c(1, n), ylim = c(0, max(x)), col = chr, cex.axis = 1.2)

# Add dashed lines for thresholds
abline(h = 0.1, col = "blue", lwd = 2, lty = 2) # Blue dashed line at 0.3
abline(h = 0.5, col = "red", lwd = 2, lty = 2)  # Red dashed line at 0.5

# Add legend for thresholds
legend("topright", legend = c("Threshold 0.1", "Threshold 0.5"), 
       col = c("blue", "red"), lwd = 2, lty = 2, cex = 1)

# Add chromosome labels on the X-axis
axis(1, at = pos, labels = chrn, las = 1, cex.axis = 0.8)

# Close the graphics device
dev.off()

My Image

9.4.2 0.5% Windows - BLUPF90+ GALLO

gwas <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_05.csv")
colnames(gwas)
colnames(gwas) <- c("X", "SNP", "rsID", "CHR", "BP", "Var")


# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_genes_w05.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_qtl_w_05.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("SNP", "rsID", "CHR", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_qtl_w_05_clean.csv")

The GALLO output are bellow:

For GENES

X.1 X SNP rsID CHR BP Var chr start_pos end_pos width strand gene_id gene_name gene_biotype
1 1 ARS-BFGL-BAC-28665 rs111010562 24 28487771 0.6899732 24 28485983 28486143 161 + ENSBTAG00000028575 U1 snRNA
2 42 Hapmap54981-rs29019846 rs29019846 24 28516684 0.7747524 24 28485983 28486143 161 + ENSBTAG00000028575 U1 snRNA
3 4 ARS-BFGL-NGS-103753 rs110842922 2 115821065 1.1689686 2 115812583 115843935 31353 - ENSBTAG00000021325 SLC19A3 protein_coding
4 41 Hapmap54770-rs29009608 rs29009608 2 115875702 0.7398417 2 115812583 115843935 31353 - ENSBTAG00000021325 SLC19A3 protein_coding
5 4 ARS-BFGL-NGS-103753 rs110842922 2 115821065 1.1689686 2 115838391 115840022 1632 + ENSBTAG00000007127 NA protein_coding
6 41 Hapmap54770-rs29009608 rs29009608 2 115875702 0.7398417 2 115838391 115840022 1632 + ENSBTAG00000007127 NA protein_coding
7 11 ARS-BFGL-NGS-43721 rs108974471 2 115986085 0.8178265 2 115948258 115951955 3698 + ENSBTAG00000021326 CCL20 protein_coding
8 11 ARS-BFGL-NGS-43721 rs108974471 2 115986085 0.8178265 2 115992779 116028253 35475 + ENSBTAG00000021327 DAW1 protein_coding
9 5 ARS-BFGL-NGS-107330 rs109766798 2 116018639 0.5958551 2 115992779 116028253 35475 + ENSBTAG00000021327 DAW1 protein_coding
10 37 Hapmap41888-BTA-49091 rs41645223 2 115622067 0.8268392 2 115629949 115704725 74777 + ENSBTAG00000021323 AGFG1 protein_coding
11 2 ARS-BFGL-BAC-35548 rs110100182 2 115665427 1.2851806 2 115629949 115704725 74777 + ENSBTAG00000021323 AGFG1 protein_coding
12 21 BTA-49096-no-rs rs41578131 2 115695003 1.6071883 2 115629949 115704725 74777 + ENSBTAG00000021323 AGFG1 protein_coding
13 8 ARS-BFGL-NGS-30337 rs110485060 2 115730530 1.3488469 2 115629949 115704725 74777 + ENSBTAG00000021323 AGFG1 protein_coding
14 8 ARS-BFGL-NGS-30337 rs110485060 2 115730530 1.3488469 2 115758046 115758105 60 + ENSBTAG00000054571 bta-mir-2285ao-1 miRNA
15 4 ARS-BFGL-NGS-103753 rs110842922 2 115821065 1.1689686 2 115791504 115808606 17103 + ENSBTAG00000050771 NA lncRNA
16 40 Hapmap50266-BTA-13664 rs29018622 13 18266073 0.6006650 13 18226732 18280982 54251 - ENSBTAG00000016060 CREM protein_coding
17 39 Hapmap49833-BTA-103929 rs41603335 13 18386942 0.5282867 13 18321888 18393476 71589 + ENSBTAG00000007133 CUL2 protein_coding
18 20 BTA-25900-no-rs rs41575397 13 18509812 0.6364128 13 18449975 18466359 16385 + ENSBTAG00000052242 NA lncRNA
19 20 BTA-25900-no-rs rs41575397 13 18509812 0.6364128 13 18484975 19062784 577810 + ENSBTAG00000014991 PARD3 protein_coding
20 3 ARS-BFGL-BAC-7444 rs110491621 13 18558540 0.6630789 13 18484975 19062784 577810 + ENSBTAG00000014991 PARD3 protein_coding
21 35 BTB-01948148 rs43056622 3 111677167 0.7467347 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
22 38 Hapmap42062-BTA-109789 rs41621207 3 111708236 1.2415855 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
23 31 BTB-01641394 rs42752353 3 111730561 0.9480214 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
24 10 ARS-BFGL-NGS-37809 rs42751504 3 111751663 1.0607739 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
25 6 ARS-BFGL-NGS-25298 rs109868537 3 111772736 1.2101267 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
26 12 ARS-BFGL-NGS-44131 rs110100483 3 111806406 1.0252690 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
27 15 ARS-BFGL-NGS-6202 rs110385521 3 111833768 1.1671762 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
28 17 ARS-BFGL-NGS-85333 rs110742206 3 111933069 0.6077153 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
29 18 ARS-BFGL-NGS-97849 rs110553601 3 111965305 0.7547885 3 111603940 112289188 685249 + ENSBTAG00000005784 CSMD2 protein_coding
30 17 ARS-BFGL-NGS-85333 rs110742206 3 111933069 0.6077153 3 111927003 111927727 725 - ENSBTAG00000000335 HMGB4 protein_coding
31 18 ARS-BFGL-NGS-97849 rs110553601 3 111965305 0.7547885 3 111927003 111927727 725 - ENSBTAG00000000335 HMGB4 protein_coding
32 7 ARS-BFGL-NGS-2713 rs41761360 15 34054485 0.5549892 15 34027052 34225316 198265 + ENSBTAG00000001410 GRAMD1B protein_coding
33 19 ARS-BFGL-NGS-98724 rs109709275 15 34109962 0.6252263 15 34027052 34225316 198265 + ENSBTAG00000001410 GRAMD1B protein_coding
34 14 ARS-BFGL-NGS-5976 rs41763278 15 34144843 0.5490537 15 34027052 34225316 198265 + ENSBTAG00000001410 GRAMD1B protein_coding
35 22 BTA-73915-no-rs rs41648979 5 6312610 0.5103844 5 6174722 6273171 98450 + ENSBTAG00000021756 ZDHHC17 protein_coding
36 22 BTA-73915-no-rs rs41648979 5 6312610 0.5103844 5 6281008 6300975 19968 - ENSBTAG00000013406 CSRP2 protein_coding
37 28 BTB-01434227 rs42557533 10 50554831 0.6660810 10 50557150 50607672 50523 + ENSBTAG00000013493 BNIP2 protein_coding
38 28 BTB-01434227 rs42557533 10 50554831 0.6660810 10 50582585 50614948 32364 + ENSBTAG00000010298 GTF2A2 protein_coding
39 28 BTB-01434227 rs42557533 10 50554831 0.6660810 10 50584841 50584967 127 - ENSBTAG00000043816 NA snoRNA

FOR QTLs

X SNP rsID CHR QTL_type start_pos end_pos QTL_ID
1 ARS-BFGL-BAC-28665 rs111010562 24 Meat_and_Carcass 28473214 28473218 232857
2 Hapmap54981-rs29019846 rs29019846 24 Meat_and_Carcass 28473214 28473218 232857
3 BTB-01485274 rs42609685 24 Health 28570243 28570247 57038
4 Hapmap58887-rs29013502 rs29013502 24 Health 28570243 28570247 57038
5 BTB-01646599 rs42761380 24 Health 28570243 28570247 57038
6 BTB-01485274 rs42609685 24 Health 28570243 28570247 57040
7 Hapmap58887-rs29013502 rs29013502 24 Health 28570243 28570247 57040
8 BTB-01646599 rs42761380 24 Health 28570243 28570247 57040
9 BTB-01485274 rs42609685 24 Production 28570243 28570247 69281
10 Hapmap58887-rs29013502 rs29013502 24 Production 28570243 28570247 69281
11 BTB-01646599 rs42761380 24 Production 28570243 28570247 69281
12 Hapmap58887-rs29013502 rs29013502 24 Reproduction 28604670 28604674 138598
13 BTB-01646599 rs42761380 24 Reproduction 28604670 28604674 138598
14 ARS-BFGL-BAC-35548 rs110100182 2 Production 115695001 115695005 283322
15 BTA-49096-no-rs rs41578131 2 Production 115695001 115695005 283322
16 ARS-BFGL-NGS-30337 rs110485060 2 Production 115695001 115695005 283322
17 ARS-BFGL-NGS-103753 rs110842922 2 Milk 115820324 115820328 215425
18 ARS-BFGL-NGS-103753 rs110842922 2 Exterior 115839213 115839217 125900
19 Hapmap54770-rs29009608 rs29009608 2 Exterior 115839213 115839217 125900
20 Hapmap54770-rs29009608 rs29009608 2 Milk 115909335 115909339 155904
21 Hapmap54770-rs29009608 rs29009608 2 Milk 115909359 115909363 155914
22 ARS-BFGL-NGS-43721 rs108974471 2 Production 115986083 115986087 39013
23 ARS-BFGL-NGS-107330 rs109766798 2 Production 115986083 115986087 39013
24 ARS-BFGL-NGS-43721 rs108974471 2 Reproduction 115986083 115986087 39014
25 ARS-BFGL-NGS-107330 rs109766798 2 Reproduction 115986083 115986087 39014
26 ARS-BFGL-NGS-43721 rs108974471 2 Exterior 115986083 115986087 39015
27 ARS-BFGL-NGS-107330 rs109766798 2 Exterior 115986083 115986087 39015
28 ARS-BFGL-NGS-43721 rs108974471 2 Exterior 115986083 115986087 39016
29 ARS-BFGL-NGS-107330 rs109766798 2 Exterior 115986083 115986087 39016
30 ARS-BFGL-NGS-43721 rs108974471 2 Milk 115986083 115986087 39017
31 ARS-BFGL-NGS-107330 rs109766798 2 Milk 115986083 115986087 39017
32 ARS-BFGL-NGS-43721 rs108974471 2 Production 115986083 115986087 39018
33 ARS-BFGL-NGS-107330 rs109766798 2 Production 115986083 115986087 39018
34 ARS-BFGL-NGS-43721 rs108974471 2 Exterior 115986083 115986087 39019
35 ARS-BFGL-NGS-107330 rs109766798 2 Exterior 115986083 115986087 39019
36 ARS-BFGL-NGS-43721 rs108974471 2 Milk 115986083 115986087 39020
37 ARS-BFGL-NGS-107330 rs109766798 2 Milk 115986083 115986087 39020
38 ARS-BFGL-NGS-43721 rs108974471 2 Production 115986083 115986087 39021
39 ARS-BFGL-NGS-107330 rs109766798 2 Production 115986083 115986087 39021
40 ARS-BFGL-NGS-43721 rs108974471 2 Production 115986083 115986087 39022
41 ARS-BFGL-NGS-107330 rs109766798 2 Production 115986083 115986087 39022
42 ARS-BFGL-NGS-43721 rs108974471 2 Milk 115986083 115986087 39023
43 ARS-BFGL-NGS-107330 rs109766798 2 Milk 115986083 115986087 39023
44 ARS-BFGL-NGS-43721 rs108974471 2 Milk 115986083 115986087 39024
45 ARS-BFGL-NGS-107330 rs109766798 2 Milk 115986083 115986087 39024
46 ARS-BFGL-NGS-43721 rs108974471 2 Exterior 115986083 115986087 39025
47 ARS-BFGL-NGS-107330 rs109766798 2 Exterior 115986083 115986087 39025
48 ARS-BFGL-NGS-43721 rs108974471 2 Exterior 115986083 115986087 39026
49 ARS-BFGL-NGS-107330 rs109766798 2 Exterior 115986083 115986087 39026
50 ARS-BFGL-NGS-43721 rs108974471 2 Reproduction 115986083 115986087 39027
51 ARS-BFGL-NGS-107330 rs109766798 2 Reproduction 115986083 115986087 39027
52 ARS-BFGL-NGS-43721 rs108974471 2 Exterior 115986083 115986087 39028
53 ARS-BFGL-NGS-107330 rs109766798 2 Exterior 115986083 115986087 39028
54 Hapmap49833-BTA-103929 rs41603335 13 Milk 18340131 18340135 116582
55 Hapmap49833-BTA-103929 rs41603335 13 Milk 18349683 18349687 116735
56 Hapmap49833-BTA-103929 rs41603335 13 Milk 18370805 18370809 249726
57 Hapmap49833-BTA-103929 rs41603335 13 Milk 18381731 18381735 116525
58 Hapmap49833-BTA-103929 rs41603335 13 Reproduction 18386940 18386944 46808
59 Hapmap49833-BTA-103929 rs41603335 13 Reproduction 18386940 18386944 46809
60 Hapmap49833-BTA-103929 rs41603335 13 Exterior 18386940 18386944 46810
61 Hapmap49833-BTA-103929 rs41603335 13 Exterior 18386940 18386944 46811
62 Hapmap49833-BTA-103929 rs41603335 13 Production 18386940 18386944 46812
63 Hapmap49833-BTA-103929 rs41603335 13 Exterior 18386940 18386944 46813
64 Hapmap49833-BTA-103929 rs41603335 13 Milk 18386940 18386944 46814
65 Hapmap49833-BTA-103929 rs41603335 13 Production 18386940 18386944 46815
66 Hapmap49833-BTA-103929 rs41603335 13 Production 18386940 18386944 46816
67 Hapmap49833-BTA-103929 rs41603335 13 Exterior 18386940 18386944 46817
68 Hapmap49833-BTA-103929 rs41603335 13 Exterior 18386940 18386944 46818
69 Hapmap49833-BTA-103929 rs41603335 13 Exterior 18386940 18386944 46819
70 Hapmap49833-BTA-103929 rs41603335 13 Reproduction 18386940 18386944 46820
71 Hapmap49833-BTA-103929 rs41603335 13 Reproduction 18386940 18386944 46821
72 Hapmap49833-BTA-103929 rs41603335 13 Exterior 18386940 18386944 46822
73 Hapmap49833-BTA-103929 rs41603335 13 Meat_and_Carcass 18386940 18386944 151596
74 Hapmap49833-BTA-103929 rs41603335 13 Meat_and_Carcass 18392222 18392226 226401
75 Hapmap49833-BTA-103929 rs41603335 13 Meat_and_Carcass 18392222 18392226 228662
76 Hapmap49833-BTA-103929 rs41603335 13 Meat_and_Carcass 18392222 18392226 234093
77 Hapmap49833-BTA-103929 rs41603335 13 Milk 18406821 18406825 116526
78 BTA-25900-no-rs rs41575397 13 Reproduction 18489668 18489672 46823
79 BTA-25900-no-rs rs41575397 13 Exterior 18489668 18489672 46824
80 BTA-25900-no-rs rs41575397 13 Exterior 18489668 18489672 46825
81 BTA-25900-no-rs rs41575397 13 Milk 18489668 18489672 46826
82 BTA-25900-no-rs rs41575397 13 Production 18489668 18489672 46827
83 BTA-25900-no-rs rs41575397 13 Exterior 18489668 18489672 46828
84 BTA-25900-no-rs rs41575397 13 Exterior 18489668 18489672 46829
85 BTA-25900-no-rs rs41575397 13 Production 18489668 18489672 46830
86 BTA-25900-no-rs rs41575397 13 Production 18489668 18489672 46831
87 BTA-25900-no-rs rs41575397 13 Exterior 18489668 18489672 46832
88 BTA-25900-no-rs rs41575397 13 Exterior 18489668 18489672 46833
89 BTA-25900-no-rs rs41575397 13 Reproduction 18489668 18489672 46834
90 BTA-25900-no-rs rs41575397 13 Reproduction 18489668 18489672 46835
91 BTA-25900-no-rs rs41575397 13 Exterior 18489668 18489672 46836
92 BTA-25900-no-rs rs41575397 13 Reproduction 18509810 18509814 46837
93 ARS-BFGL-BAC-7444 rs110491621 13 Reproduction 18509810 18509814 46837
94 BTA-25900-no-rs rs41575397 13 Reproduction 18509810 18509814 46838
95 ARS-BFGL-BAC-7444 rs110491621 13 Reproduction 18509810 18509814 46838
96 BTA-25900-no-rs rs41575397 13 Exterior 18509810 18509814 46839
97 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18509810 18509814 46839
98 BTA-25900-no-rs rs41575397 13 Exterior 18509810 18509814 46840
99 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18509810 18509814 46840
100 BTA-25900-no-rs rs41575397 13 Production 18509810 18509814 46841
101 ARS-BFGL-BAC-7444 rs110491621 13 Production 18509810 18509814 46841
102 BTA-25900-no-rs rs41575397 13 Exterior 18509810 18509814 46842
103 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18509810 18509814 46842
104 BTA-25900-no-rs rs41575397 13 Exterior 18509810 18509814 46843
105 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18509810 18509814 46843
106 BTA-25900-no-rs rs41575397 13 Production 18509810 18509814 46844
107 ARS-BFGL-BAC-7444 rs110491621 13 Production 18509810 18509814 46844
108 BTA-25900-no-rs rs41575397 13 Production 18509810 18509814 46845
109 ARS-BFGL-BAC-7444 rs110491621 13 Production 18509810 18509814 46845
110 BTA-25900-no-rs rs41575397 13 Exterior 18509810 18509814 46846
111 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18509810 18509814 46846
112 BTA-25900-no-rs rs41575397 13 Exterior 18509810 18509814 46847
113 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18509810 18509814 46847
114 BTA-25900-no-rs rs41575397 13 Exterior 18509810 18509814 46848
115 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18509810 18509814 46848
116 BTA-25900-no-rs rs41575397 13 Exterior 18509810 18509814 46849
117 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18509810 18509814 46849
118 BTA-25900-no-rs rs41575397 13 Reproduction 18509810 18509814 46850
119 ARS-BFGL-BAC-7444 rs110491621 13 Reproduction 18509810 18509814 46850
120 BTA-25900-no-rs rs41575397 13 Health 18509810 18509814 46851
121 ARS-BFGL-BAC-7444 rs110491621 13 Health 18509810 18509814 46851
122 BTA-25900-no-rs rs41575397 13 Reproduction 18509810 18509814 46852
123 ARS-BFGL-BAC-7444 rs110491621 13 Reproduction 18509810 18509814 46852
124 BTA-25900-no-rs rs41575397 13 Exterior 18509810 18509814 46853
125 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18509810 18509814 46853
126 BTA-25900-no-rs rs41575397 13 Reproduction 18558538 18558542 46854
127 ARS-BFGL-BAC-7444 rs110491621 13 Reproduction 18558538 18558542 46854
128 BTA-25900-no-rs rs41575397 13 Exterior 18558538 18558542 46855
129 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18558538 18558542 46855
130 BTA-25900-no-rs rs41575397 13 Exterior 18558538 18558542 46856
131 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18558538 18558542 46856
132 BTA-25900-no-rs rs41575397 13 Milk 18558538 18558542 46857
133 ARS-BFGL-BAC-7444 rs110491621 13 Milk 18558538 18558542 46857
134 BTA-25900-no-rs rs41575397 13 Exterior 18558538 18558542 46858
135 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18558538 18558542 46858
136 BTA-25900-no-rs rs41575397 13 Milk 18558538 18558542 46859
137 ARS-BFGL-BAC-7444 rs110491621 13 Milk 18558538 18558542 46859
138 BTA-25900-no-rs rs41575397 13 Production 18558538 18558542 46860
139 ARS-BFGL-BAC-7444 rs110491621 13 Production 18558538 18558542 46860
140 BTA-25900-no-rs rs41575397 13 Production 18558538 18558542 46861
141 ARS-BFGL-BAC-7444 rs110491621 13 Production 18558538 18558542 46861
142 BTA-25900-no-rs rs41575397 13 Milk 18558538 18558542 46862
143 ARS-BFGL-BAC-7444 rs110491621 13 Milk 18558538 18558542 46862
144 BTA-25900-no-rs rs41575397 13 Exterior 18558538 18558542 46863
145 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18558538 18558542 46863
146 BTA-25900-no-rs rs41575397 13 Exterior 18558538 18558542 46864
147 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18558538 18558542 46864
148 BTA-25900-no-rs rs41575397 13 Reproduction 18558538 18558542 46865
149 ARS-BFGL-BAC-7444 rs110491621 13 Reproduction 18558538 18558542 46865
150 BTA-25900-no-rs rs41575397 13 Reproduction 18558538 18558542 46866
151 ARS-BFGL-BAC-7444 rs110491621 13 Reproduction 18558538 18558542 46866
152 BTA-25900-no-rs rs41575397 13 Exterior 18558538 18558542 46867
153 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18558538 18558542 46867
154 BTA-25900-no-rs rs41575397 13 Exterior 18558538 18558542 46868
155 ARS-BFGL-BAC-7444 rs110491621 13 Exterior 18558538 18558542 46868
156 BTB-01948148 rs43056622 3 Reproduction 111708234 111708238 30008
157 Hapmap42062-BTA-109789 rs41621207 3 Reproduction 111708234 111708238 30008
158 BTB-01641394 rs42752353 3 Reproduction 111708234 111708238 30008
159 ARS-BFGL-NGS-37809 rs42751504 3 Reproduction 111708234 111708238 30008
160 BTB-01948148 rs43056622 3 Reproduction 111708234 111708238 30244
161 Hapmap42062-BTA-109789 rs41621207 3 Reproduction 111708234 111708238 30244
162 BTB-01641394 rs42752353 3 Reproduction 111708234 111708238 30244
163 ARS-BFGL-NGS-37809 rs42751504 3 Reproduction 111708234 111708238 30244
164 BTB-01948148 rs43056622 3 Meat_and_Carcass 111708234 111708238 152258
165 Hapmap42062-BTA-109789 rs41621207 3 Meat_and_Carcass 111708234 111708238 152258
166 BTB-01641394 rs42752353 3 Meat_and_Carcass 111708234 111708238 152258
167 ARS-BFGL-NGS-37809 rs42751504 3 Meat_and_Carcass 111708234 111708238 152258
168 BTB-01948148 rs43056622 3 Meat_and_Carcass 111717521 111717525 225527
169 Hapmap42062-BTA-109789 rs41621207 3 Meat_and_Carcass 111717521 111717525 225527
170 BTB-01641394 rs42752353 3 Meat_and_Carcass 111717521 111717525 225527
171 ARS-BFGL-NGS-37809 rs42751504 3 Meat_and_Carcass 111717521 111717525 225527
172 ARS-BFGL-NGS-97849 rs110553601 3 Meat_and_Carcass 111991214 111991218 151880
173 ARS-BFGL-NGS-97849 rs110553601 3 Meat_and_Carcass 111991214 111991218 152096
174 BTB-02063964 rs43172105 15 Reproduction 10936000 10936004 47797
175 BTB-01830390 rs42938737 15 Reproduction 10936000 10936004 47797
176 BTB-01608944 rs42723390 15 Reproduction 10936000 10936004 47797
177 BTB-02063964 rs43172105 15 Reproduction 10936000 10936004 47798
178 BTB-01830390 rs42938737 15 Reproduction 10936000 10936004 47798
179 BTB-01608944 rs42723390 15 Reproduction 10936000 10936004 47798
180 BTB-02063964 rs43172105 15 Exterior 10936000 10936004 47799
181 BTB-01830390 rs42938737 15 Exterior 10936000 10936004 47799
182 BTB-01608944 rs42723390 15 Exterior 10936000 10936004 47799
183 BTB-02063964 rs43172105 15 Milk 10936000 10936004 47800
184 BTB-01830390 rs42938737 15 Milk 10936000 10936004 47800
185 BTB-01608944 rs42723390 15 Milk 10936000 10936004 47800
186 BTB-02063964 rs43172105 15 Milk 10936000 10936004 47801
187 BTB-01830390 rs42938737 15 Milk 10936000 10936004 47801
188 BTB-01608944 rs42723390 15 Milk 10936000 10936004 47801
189 BTB-02063964 rs43172105 15 Production 10936000 10936004 47802
190 BTB-01830390 rs42938737 15 Production 10936000 10936004 47802
191 BTB-01608944 rs42723390 15 Production 10936000 10936004 47802
192 BTB-02063964 rs43172105 15 Production 10936000 10936004 47803
193 BTB-01830390 rs42938737 15 Production 10936000 10936004 47803
194 BTB-01608944 rs42723390 15 Production 10936000 10936004 47803
195 BTB-02063964 rs43172105 15 Milk 10936000 10936004 47804
196 BTB-01830390 rs42938737 15 Milk 10936000 10936004 47804
197 BTB-01608944 rs42723390 15 Milk 10936000 10936004 47804
198 BTB-02063964 rs43172105 15 Milk 10936000 10936004 47805
199 BTB-01830390 rs42938737 15 Milk 10936000 10936004 47805
200 BTB-01608944 rs42723390 15 Milk 10936000 10936004 47805
201 BTB-02063964 rs43172105 15 Reproduction 10936000 10936004 47806
202 BTB-01830390 rs42938737 15 Reproduction 10936000 10936004 47806
203 BTB-01608944 rs42723390 15 Reproduction 10936000 10936004 47806
204 BTB-02063964 rs43172105 15 Health 10936000 10936004 47807
205 BTB-01830390 rs42938737 15 Health 10936000 10936004 47807
206 BTB-01608944 rs42723390 15 Health 10936000 10936004 47807
207 BTB-02063964 rs43172105 15 Reproduction 10936000 10936004 47808
208 BTB-01830390 rs42938737 15 Reproduction 10936000 10936004 47808
209 BTB-01608944 rs42723390 15 Reproduction 10936000 10936004 47808
210 BTB-02063964 rs43172105 15 Exterior 10936000 10936004 47809
211 BTB-01830390 rs42938737 15 Exterior 10936000 10936004 47809
212 BTB-01608944 rs42723390 15 Exterior 10936000 10936004 47809
213 BTB-02063964 rs43172105 15 Exterior 10936000 10936004 47810
214 BTB-01830390 rs42938737 15 Exterior 10936000 10936004 47810
215 BTB-01608944 rs42723390 15 Exterior 10936000 10936004 47810
216 BTB-02063964 rs43172105 15 Exterior 10936000 10936004 47811
217 BTB-01830390 rs42938737 15 Exterior 10936000 10936004 47811
218 BTB-01608944 rs42723390 15 Exterior 10936000 10936004 47811
219 BTB-02063964 rs43172105 15 Reproduction 10936000 10936004 281490
220 BTB-01830390 rs42938737 15 Reproduction 10936000 10936004 281490
221 BTB-01608944 rs42723390 15 Reproduction 10936000 10936004 281490
222 BTB-01421892 rs42544714 15 Meat_and_Carcass 11198692 11198696 226564
223 BTB-01421934 rs42545356 15 Meat_and_Carcass 11198692 11198696 226564
224 BTB-01422008 rs42545430 15 Meat_and_Carcass 11198692 11198696 226564
225 ARS-BFGL-NGS-2713 rs41761360 15 Meat_and_Carcass 34025602 34025606 152600
226 ARS-BFGL-NGS-98724 rs109709275 15 Reproduction 34139603 34139607 62361
227 ARS-BFGL-NGS-5976 rs41763278 15 Reproduction 34139603 34139607 62361
228 ARS-BFGL-NGS-98724 rs109709275 15 Reproduction 34139603 34139607 62407
229 ARS-BFGL-NGS-5976 rs41763278 15 Reproduction 34139603 34139607 62407
230 ARS-BFGL-NGS-98724 rs109709275 15 Reproduction 34139603 34139607 62411
231 ARS-BFGL-NGS-5976 rs41763278 15 Reproduction 34139603 34139607 62411
232 ARS-BFGL-NGS-5976 rs41763278 15 Milk 34161889 34161893 155975
233 ARS-BFGL-NGS-5976 rs41763278 15 Reproduction 34164294 34164298 62410
234 ARS-BFGL-NGS-3276 rs110634531 20 Health 12088254 12088258 179019
235 ARS-BFGL-NGS-78615 rs110959523 20 Health 12088254 12088258 179019
236 BTA-73915-no-rs rs41648979 5 Reproduction 6318394 6318398 212544
237 BTB-01434227 rs42557533 10 Health 50597586 50597590 156580

QTL Plots

# Set working directory
setwd("/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10")

#QTL type plot
oldpar <- par(mar=c(1,15,0.5,1))
plot_qtl_info(out.qtl, qtl_plot = "qtl_type", cex=1.5)

#QTL names plot (by type)

# Set the width and height in pixels for 600 DPI
png("qtl_names_w05.png", width=5100, height=6600, res=600)  # Width and height in pixels
# Set layout for multiple plots
par(mfrow=c(6, 1), mar=c(2, 20, 1, 1))
# List of QTL classes for titles
qtl_classes <- c("Production", "Reproduction", "Milk", "Meat_and_Carcass", "Health", "Exterior")
# Loop through each QTL class and plot
for (qtl_class in qtl_classes) {
  plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class=qtl_class)
  title(main=qtl_class)  # Add the QTL class as the main title for each plot
}
# Close the device
dev.off()


#QTL enrichment analysis 

out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]
write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_enrich_qtl_genome_name_w05.csv")


out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_enrich_qtl_genome_type_w05.csv")


#Plots

#Name
#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL, out.enrich_qtl_name$CHR,sep="")
#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]
#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type
#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL, out.enrich_qtl_type$CHR,sep="")
#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]
#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL type My Image

QTL name by type My Image

9.4.2.1 Windows - QTL enrichment on GALLO

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X QTL N_QTLs N_QTLs_db Total_annotated_QTLs Total_QTLs_db pvalue adj.pval QTL_type
11 Foot angle 6 672 128 163224 0.0000169 0.0005655 Exterior
32 Rear leg placement - side view 5 430 128 163224 0.0000251 0.0005655 Exterior
6 Calving ease 12 3819 128 163224 0.0000513 0.0005773 Reproduction
31 Rear leg placement - rear view 5 491 128 163224 0.0000471 0.0005773 Exterior
28 Net merit 6 903 128 163224 0.0000863 0.0007764 Production
37 Stillbirth 7 1363 128 163224 0.0001097 0.0008227 Reproduction
9 Feet and leg conformation 5 627 128 163224 0.0001476 0.0009488 Exterior
44 Udder depth 5 695 128 163224 0.0002371 0.0013337 Exterior
30 PTA type 4 627 128 163224 0.0015793 0.0078963 Production
43 Udder attachment 4 655 128 163224 0.0018503 0.0083263 Exterior
25 Milk tridecylic acid content 3 319 128 163224 0.0021074 0.0084756 Milk
40 Teat placement - front 3 327 128 163224 0.0022602 0.0084756 Exterior
10 Fertility index 2 108 128 163224 0.0033389 0.0115577 Reproduction
12 Head width 1 6 128 163224 0.0046960 0.0150944 Production
17 Length of productive life 6 2004 128 163224 0.0051861 0.0155583 Production
33 Respiratory rate 1 9 128 163224 0.0070359 0.0197884 Health
34 Rump conformation 1 13 128 163224 0.0101471 0.0268600 Exterior
38 Strength 3 664 128 163224 0.0157176 0.0392939 Exterior
3 Body temperature 1 23 128 163224 0.0178830 0.0423545 Health
26 Muscle calcium content 1 40 128 163224 0.0308966 0.0695174 Meat and Carcass
27 Muscle sodium content 1 56 128 163224 0.0429884 0.0921180 Meat and Carcass
14 Interval from first to last insemination 2 445 128 163224 0.0481346 0.0984572 Reproduction
15 Interval to first estrus after calving 3 1053 128 163224 0.0505607 0.0989232 Reproduction
45 Udder height 2 504 128 163224 0.0599662 0.1124366 Exterior
16 Intramuscular fat 1 117 128 163224 0.0877305 0.1579149 Meat and Carcass
8 Fat thickness at the 12th rib 1 169 128 163224 0.1242283 0.2150105 Meat and Carcass
22 Milk glycosylated kappa-casein percentage 4 2527 128 163224 0.1380822 0.2301370 Milk
1 Age at first calving 1 233 128 163224 0.1671652 0.2686584 Reproduction
39 Teat length 1 300 128 163224 0.2098779 0.3148168 Exterior
41 Teat placement - rear 1 298 128 163224 0.2086349 0.3148168 Exterior
36 Somatic cell score 2 1122 128 163224 0.2199895 0.3193396 Health
18 M. paratuberculosis susceptibility 1 492 128 163224 0.3206093 0.4508568 Health
2 Body depth 1 616 128 163224 0.3837908 0.5233511 Production
19 Marbling score 2 1817 128 163224 0.4175991 0.5369131 Meat and Carcass
35 Shear force 3 2954 128 163224 0.4091307 0.5369131 Meat and Carcass
24 Milk protein yield 3 3093 128 163224 0.4380445 0.5475556 Milk
13 Inseminations per conception 1 790 128 163224 0.4627346 0.5627854 Reproduction
29 Pregnancy rate 1 944 128 163224 0.5241831 0.6207431 Reproduction
5 Bovine tuberculosis susceptibility 1 1155 128 163224 0.5972036 0.6890811 Health
4 Body weight 1 4289 128 163224 0.9669506 0.9713008 Production
7 Connective tissue amount 1 3142 128 163224 0.9170032 0.9713008 Meat and Carcass
20 Milk fat percentage 5 10941 128 163224 0.9357122 0.9713008 Milk
21 Milk fat yield 4 8220 128 163224 0.8906967 0.9713008 Milk
23 Milk protein percentage 3 8803 128 163224 0.9713008 0.9713008 Milk
42 Tenderness score 1 3483 128 163224 0.9368355 0.9713008 Meat and Carcass

My Image

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X QTL N_QTLs N_QTLs_db Total_annotated_QTLs Total_QTLs_db pvalue adj.pval
1 Exterior 41 9077 128 163224 0.0000000 0.0000000
2 Health 6 5889 128 163224 0.3161000 0.6049498
3 Meat and Carcass 11 18258 128 163224 0.8597556 1.0000000
4 Milk 22 75352 128 163224 1.0000000 1.0000000
5 Production 19 19640 128 163224 0.1968827 0.5906482
6 Reproduction 29 35008 128 163224 0.4032999 0.6049498

My Image

9.4.3 0.1% Windows - BLUPF90+ GALLO

gwas <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_01.csv")

colnames(gwas)
colnames(gwas) <- c("X", "CHR", "BP", "SNP", "Var")


# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")

#import a gene annotation file
gene_UDC1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Bos_taurus.ARS-UCD1.2.110.gtf.gz",file_type="gtf")

#FINDING GENES AND QTLs ARROUND THE MARKER

#FINDING GENES
out.genes <- find_genes_qtls_around_markers(db_file= gene_UDC1_2, 
                                            marker_file= gwas, 
                                            method = "gene",
                                            marker = "snp", 
                                            interval = 50000, 
                                            nThreads = NULL)

write.csv(out.genes, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_genes_w01.csv")

#FINDING QTLs

out.qtl <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)


write.table(out.qtl, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_qtl_w_01.txt", 
            quote = FALSE, sep = "\t", row.names = FALSE, col.names = T)

library(tidyverse)
out.qtl.clean <- select(out.qtl, c("SNP", "CHR", "QTL_type", "start_pos", "end_pos","QTL_ID"))
write.csv(out.qtl.clean, file = "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_qtl_w_01_clean.csv")

table(out.genes$gene_biotype)

The GALLO output are bellow:

For GENES Because this file has 1,608 rows we’ll not show here, but you can take a look in the file: /home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_genes_w01.csv

FOR QTLs Because this file has 7,347 rows we’ll not show here, but you can take a look in the file: /home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_qtl_w_01_clean.csv

QTL Plots

# Set working directory
setwd("/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10")

#QTL type plot
oldpar <- par(mar=c(1,10,1,1))
plot_qtl_info(out.qtl, qtl_plot = "qtl_type", cex=1.5)

#QTL names plot (by type)

# Set the width and height in pixels for 600 DPI
png("qtl_names_w01.png", width=5100, height=6600, res=600)  # Width and height in pixels
# Set layout for multiple plots
par(mfrow=c(6, 1), mar=c(2, 20, 1, 1))
# List of QTL classes for titles
qtl_classes <- c("Production", "Reproduction", "Milk", "Meat_and_Carcass", "Health", "Exterior")
# Loop through each QTL class and plot
for (qtl_class in qtl_classes) {
  plot_qtl_info(out.qtl, qtl_plot = "qtl_name", qtl_class=qtl_class)
  title(main=qtl_class)  # Add the QTL class as the main title for each plot
}
# Close the device
dev.off()


#QTL enrichment analysis 

out.enrich_qtl_name <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "Name",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)


# Sorting the dataframe in ascending order of adj.pval
sorted_df <- out.enrich_qtl_name[order(out.enrich_qtl_name$adj.pval), ]
write.csv(sorted_df,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_enrich_qtl_genome_name_w01.csv")


out.enrich_qtl_type <-qtl_enrich(qtl_db= qtl_UCD1_2, 
                                 qtl_file= out.qtl, qtl_type = "QTL_type",
                                 enrich_type = "genome", chr.subset = NULL, 
                                 padj = "fdr",nThreads = 2)

sorted_df_type <- out.enrich_qtl_type[order(out.enrich_qtl_type$adj.pval), ]
write.csv(out.enrich_qtl_type,"/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_enrich_qtl_genome_type_w01.csv")


#Plots

#Name
#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_name$ID<-paste(out.enrich_qtl_name$QTL, out.enrich_qtl_name$CHR,sep="")
#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered<-out.enrich_qtl_name[which(out.enrich_qtl_name$adj.pval<0.05),]
#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered, x="ID", pval="adj.pval")


#Type
#Creating a new ID composed by the trait and the chromosome
out.enrich_qtl_type$ID<-paste(out.enrich_qtl_type$QTL, out.enrich_qtl_type$CHR,sep="")
#Match the QTL classes and filtering the Reproduction related QTLs
out.enrich.filtered_type<-out.enrich_qtl_type[which(out.enrich_qtl_type$adj.pval<0.05),]
#Plotting the enrichment results for the QTL enrichment analysis
dev.off()
QTLenrich_plot(out.enrich.filtered_type, x="ID", pval="adj.pval")

QTL type My Image

QTL name by type My Image

9.4.3.1 Windows - QTL enrichment on GALLO

QTL Enrichment outcomes

Enrichment by name (enrichment analysis will be performed for each trait individually)

X QTL N_QTLs N_QTLs_db Total_annotated_QTLs Total_QTLs_db pvalue adj.pval QTL_type
172 Non-return rate 227 2312 4461 163224 0.0000000 0.0000000 Reproduction
126 Milk myristic acid content 131 902 4461 163224 0.0000000 0.0000000 Milk
33 Clinical mastitis 84 466 4461 163224 0.0000000 0.0000000 Health
10 Average daily gain 218 3140 4461 163224 0.0000000 0.0000000 Production
168 Myristic acid content 41 120 4461 163224 0.0000000 0.0000000 Meat and Carcass
120 Milk lauric acid content 60 366 4461 163224 0.0000000 0.0000000 Milk
173 Oleic acid content 31 101 4461 163224 0.0000000 0.0000000 Meat and Carcass
135 Milk potassium content 67 570 4461 163224 0.0000000 0.0000000 Milk
76 Length of productive life 130 2004 4461 163224 0.0000000 0.0000000 Production
96 Milk butyric acid content 71 828 4461 163224 0.0000000 0.0000000 Milk
100 Milk capric acid content 73 912 4461 163224 0.0000000 0.0000000 Milk
134 Milk phosphorylated alpha-S2-casein percentage 31 228 4461 163224 0.0000000 0.0000000 Milk
170 Myristoleic acid content 16 68 4461 163224 0.0000000 0.0000000 Meat and Carcass
175 Palmitoleic acid content 12 46 4461 163224 0.0000000 0.0000000 Meat and Carcass
174 Palmitic acid content 7 12 4461 163224 0.0000000 0.0000001 Meat and Carcass
139 Milk protein yield 138 3093 4461 163224 0.0000000 0.0000004 Milk
95 Milk beta-lactoglobulin protein content 20 177 4461 163224 0.0000001 0.0000013 Milk
9 Atherogenic index 5 8 4461 163224 0.0000008 0.0000096 Meat and Carcass
22 Bovine respiratory disease susceptibility 46 789 4461 163224 0.0000020 0.0000232 Health
73 Ketosis 30 441 4461 163224 0.0000064 0.0000695 Health
89 Metabolic body weight 154 4039 4461 163224 0.0000314 0.0003240 Production
114 Milk glycosylated kappa-casein percentage 102 2527 4461 163224 0.0000869 0.0008570 Milk
92 Milk alpha-S2-casein percentage 18 235 4461 163224 0.0000973 0.0009176 Milk
154 Monounsaturated fatty acid content 8 60 4461 163224 0.0002232 0.0020185 Meat and Carcass
47 Eye area pigmentation 7 49 4461 163224 0.0003560 0.0030899 Exterior
62 Infectious bovine keratoconjunctivitis susceptibility 5 24 4461 163224 0.0004188 0.0034953 Health
148 Milk unsaturated fatty acid content 6 38 4461 163224 0.0005409 0.0043476 Milk
187 Saturated fatty acid content 8 71 4461 163224 0.0007124 0.0055211 Meat and Carcass
155 Monounsaturated to saturated fatty acid ratio 2 2 4461 163224 0.0007468 0.0055881 Meat and Carcass
141 Milk saturated fatty acid content 8 80 4461 163224 0.0015644 0.0113159 Milk
16 Body temperature 4 23 4461 163224 0.0032549 0.0227840 Health
88 Medium-chain fatty acid content 2 6 4461 163224 0.0104106 0.0705968 Meat and Carcass
15 Body length 4 32 4461 163224 0.0108840 0.0715709 Production
36 Cooking loss 3 18 4461 163224 0.0122425 0.0775241 Meat and Carcass
196 Sperm motility 7 91 4461 163224 0.0125039 0.0775241 Reproduction
24 Calf mortality 2 7 4461 163224 0.0143119 0.0862687 Reproduction
160 Muscle creatine content 5 54 4461 163224 0.0158868 0.0931739 Meat and Carcass
48 Facial pigmentation 4 36 4461 163224 0.0163639 0.0934463 Exterior
171 Net merit 36 903 4461 163224 0.0173687 0.0966411 Production
182 Respiratory rate 2 9 4461 163224 0.0236603 0.1283573 Health
57 General health index 1 1 4461 163224 0.0273305 0.1289288 Health
85 Marfan syndrome-like disease 1 1 4461 163224 0.0273305 0.1289288 Health
102 Milk caprylic acid content 32 811 4461 163224 0.0268019 0.1289288 Milk
195 Sperm head abnormalities 1 1 4461 163224 0.0273305 0.1289288 Reproduction
197 Sperm tail abnormalities 1 1 4461 163224 0.0273305 0.1289288 Reproduction
215 Unsaturated fatty acid content 1 1 4461 163224 0.0273305 0.1289288 Meat and Carcass
54 Fertility index 7 108 4461 163224 0.0290780 0.1342538 Reproduction
6 Alkaline phosphatase level 2 11 4461 163224 0.0348657 0.1544055 Health
199 Stayability 2 11 4461 163224 0.0348657 0.1544055 Reproduction
213 Udder height 21 504 4461 163224 0.0393172 0.1706368 Exterior
110 Milk fat content 6 92 4461 163224 0.0406206 0.1728366 Milk
142 Milk saturated to unsaturated fatty acid ratio 3 29 4461 163224 0.0439516 0.1834135 Milk
177 Pregnancy rate 35 944 4461 163224 0.0456939 0.1870863 Reproduction
64 Inseminations per conception 30 790 4461 163224 0.0471343 0.1894099 Reproduction
113 Milk fever 1 2 4461 163224 0.0539143 0.2089178 Health
169 Myristic and palmitic acid ratio 1 2 4461 163224 0.0539143 0.2089178 Meat and Carcass
146 Milk tridecylic acid content 14 319 4461 163224 0.0577579 0.2198854 Milk
78 Liver abscess 3 33 4461 163224 0.0606176 0.2267933 Health
178 PTA type 24 627 4461 163224 0.0646760 0.2360992 Production
200 Stillbirth 47 1363 4461 163224 0.0652809 0.2360992 Reproduction
212 Udder depth 26 695 4461 163224 0.0697443 0.2481067 Exterior
34 Conception rate 43 1255 4461 163224 0.0805665 0.2775068 Reproduction
106 Milk cis-12-Octadecenoic acid content 1 3 4461 163224 0.0797716 0.2775068 Milk
7 Angularity 3 38 4461 163224 0.0849337 0.2843841 Exterior
180 Rear leg placement - side view 17 430 4461 163224 0.0851842 0.2843841 Exterior
209 Udder attachment 24 655 4461 163224 0.0934154 0.3071384 Exterior
53 Feet and leg conformation 23 627 4461 163224 0.0978354 0.3168699 Exterior
18 Body weight gain 45 1354 4461 163224 0.1071471 0.3260573 Production
52 Feed intake 4 66 4461 163224 0.1065880 0.3260573 Production
83 Male fertility 2 20 4461 163224 0.1026069 0.3260573 Reproduction
122 Milk long-chain fatty acid content 1 4 4461 163224 0.1049224 0.3260573 Milk
211 Udder cleft 18 477 4461 163224 0.1081849 0.3260573 Exterior
68 Interdigital hyperplasia 5 93 4461 163224 0.1116235 0.3273285 Exterior
191 Sole ulcer 5 93 4461 163224 0.1116235 0.3273285 Exterior
50 Fecal larva count 6 125 4461 163224 0.1286352 0.3472694 Health
51 Feed conversion ratio 2 22 4461 163224 0.1204428 0.3472694 Production
79 Long-chain fatty acid content 1 5 4461 163224 0.1293860 0.3472694 Meat and Carcass
108 Milk delta-9-desaturase content 1 5 4461 163224 0.1293860 0.3472694 Milk
153 Monocyte number 1 5 4461 163224 0.1293860 0.3472694 Health
189 Semen volume 2 23 4461 163224 0.1296259 0.3472694 Reproduction
210 Udder balance 1 5 4461 163224 0.1293860 0.3472694 Exterior
5 Age at second calving 1 6 4461 163224 0.1531811 0.3865150 Reproduction
19 Bone quality 2 25 4461 163224 0.1484422 0.3865150 Exterior
59 Head width 1 6 4461 163224 0.1531811 0.3865150 Production
132 Milk phosphocholine level 1 6 4461 163224 0.1531811 0.3865150 Milk
183 Retained placenta 1 6 4461 163224 0.1531811 0.3865150 Reproduction
8 Anti-Müllerian hormone level 2 26 4461 163224 0.1580454 0.3899729 Health
138 Milk protein percentage 256 8803 4461 163224 0.1581457 0.3899729 Milk
56 Foot angle 23 672 4461 163224 0.1627126 0.3967261 Exterior
152 Milking speed 33 1011 4461 163224 0.1719461 0.4145811 Milk
45 Duration of inactivity during novel object test 1 7 4461 163224 0.1763259 0.4188389 Exterior
145 Milk tricosanoic acid content 2 28 4461 163224 0.1775723 0.4188389 Milk
193 Somatic cell score 36 1122 4461 163224 0.1853615 0.4325102 Health
29 Carcass weight 62 2020 4461 163224 0.1922266 0.4406949 Meat and Carcass
179 Rear leg placement - rear view 17 491 4461 163224 0.1929309 0.4406949 Exterior
123 Milk margaric acid content 3 59 4461 163224 0.2184358 0.4937558 Milk
65 Insulin level 1 9 4461 163224 0.2207356 0.4938106 Health
37 Curd firmness 4 89 4461 163224 0.2263612 0.4990652 Milk
194 sperm counts 2 33 4461 163224 0.2276841 0.4990652 Reproduction
2 Adrenocorticotropic hormone level 2 34 4461 163224 0.2378405 0.5002046 Health
11 Biceps brachii weight 1 10 4461 163224 0.2420345 0.5002046 Meat and Carcass
67 Interdigital dermatitis 1 10 4461 163224 0.2420345 0.5002046 Exterior
128 Milk oleic acid content 34 1089 4461 163224 0.2382404 0.5002046 Milk
140 Milk riboflavin content 17 509 4461 163224 0.2336729 0.5002046 Milk
214 Udder width 1 10 4461 163224 0.2420345 0.5002046 Exterior
40 Dairy form 17 514 4461 163224 0.2455918 0.5027680 Exterior
60 Hip width 2 36 4461 163224 0.2582100 0.5188108 Production
181 Residual feed intake 14 418 4461 163224 0.2570187 0.5188108 Production
186 Rump width 17 526 4461 163224 0.2751345 0.5477448 Production
87 Mean corpuscular hemoglobin concentration 1 12 4461 163224 0.2829021 0.5481229 Health
201 Strength 21 664 4461 163224 0.2787307 0.5481229 Exterior
208 Time to curd firmness 1 12 4461 163224 0.2829021 0.5481229 Milk
149 Milk vitamin B-12 content 2 39 4461 163224 0.2887812 0.5545622 Milk
158 Muscle calcium content 2 40 4461 163224 0.2989460 0.5657887 Meat and Carcass
205 Teat placement - rear 10 298 4461 163224 0.2998420 0.5657887 Exterior
185 Rump conformation 1 13 4461 163224 0.3025022 0.5658878 Exterior
31 Cheese protein recovery 2 42 4461 163224 0.3191965 0.5820642 Milk
162 Muscle magnesium content 2 42 4461 163224 0.3191965 0.5820642 Meat and Carcass
217 Yield grade 2 42 4461 163224 0.3191965 0.5820642 Meat and Carcass
118 Milk lactose content 4 105 4461 163224 0.3231725 0.5844036 Milk
46 Ear size 2 44 4461 163224 0.3393017 0.6079144 Exterior
157 Muscle anserine content 4 108 4461 163224 0.3417767 0.6079144 Meat and Carcass
12 Birth index 1 17 4461 163224 0.3756910 0.6521996 Reproduction
43 Docosahexaenoic acid content 1 17 4461 163224 0.3756910 0.6521996 Meat and Carcass
55 First service conception 16 527 4461 163224 0.3700692 0.6521996 Reproduction
39 Curd yield 1 18 4461 163224 0.3927555 0.6764122 Milk
72 Intramuscular fat 4 117 4461 163224 0.3975766 0.6778472 Meat and Carcass
198 Stature 27 929 4461 163224 0.3998361 0.6778472 Exterior
203 Teat length 9 300 4461 163224 0.4356308 0.7328053 Exterior
32 Chest width 1 22 4461 163224 0.4564758 0.7337426 Production
74 Lactation persistency 7 231 4461 163224 0.4446153 0.7337426 Milk
90 Methane production 3 90 4461 163224 0.4474954 0.7337426 Production
117 Milk lactoferrin content 1 22 4461 163224 0.4564758 0.7337426 Milk
124 Milk mid-infrared spectra 2 55 4461 163224 0.4455939 0.7337426 Milk
165 Muscle sodium content 2 56 4461 163224 0.4547957 0.7337426 Meat and Carcass
14 Body height 5 166 4461 163224 0.4763337 0.7532300 Production
17 Body weight 118 4289 4461 163224 0.4837775 0.7532300 Production
28 Cannon bone circumference 1 25 4461 163224 0.4998393 0.7532300 Production
38 Curd solids yield 1 25 4461 163224 0.4998393 0.7532300 Milk
103 Milk casein index 1 24 4461 163224 0.4857834 0.7532300 Milk
136 Milk profitability index 1 25 4461 163224 0.4998393 0.7532300 Milk
161 Muscle iron content 2 60 4461 163224 0.4906960 0.7532300 Meat and Carcass
176 Perinatal mortality 1 24 4461 163224 0.4857834 0.7532300 Reproduction
192 Somatic cell count 4 132 4461 163224 0.4882405 0.7532300 Health
13 Body depth 17 616 4461 163224 0.5176377 0.7746716 Production
71 Intestinal atresia 2 65 4461 163224 0.5334108 0.7860699 Health
75 Lean meat yield 17 621 4461 163224 0.5310670 0.7860699 Meat and Carcass
109 Milk eicosapentaenoic acid content 1 28 4461 163224 0.5397439 0.7860699 Milk
204 Teat placement - front 9 327 4461 163224 0.5378775 0.7860699 Exterior
105 Milk cholesterol content 1 29 4461 163224 0.5523251 0.7990303 Milk
125 Milk monounsaturated fatty acid content 1 32 4461 163224 0.5880430 0.8450684 Milk
26 Calf sucking reflex 1 33 4461 163224 0.5993042 0.8555856 Exterior
151 Milk zinc content 10 384 4461 163224 0.6049225 0.8579619 Milk
119 Milk lactose yield 1 35 4461 163224 0.6209118 0.8637042 Milk
137 Milk protein content 1 35 4461 163224 0.6209118 0.8637042 Milk
163 Muscle phosphorus content 1 35 4461 163224 0.6209118 0.8637042 Meat and Carcass
30 Cheese fat recovery 1 36 4461 163224 0.6312747 0.8725261 Milk
1 Abomasum displacement 2 85 4461 163224 0.6786822 0.9321141 Health
116 Milk kappa-casein percentage 118 4499 4461 163224 0.6900415 0.9358687 Milk
167 Muscle zinc content 1 42 4461 163224 0.6877680 0.9358687 Meat and Carcass
77 Lifetime profit index 1 49 4461 163224 0.7428356 0.9950329 Production
156 Multiple birth 2 96 4461 163224 0.7415251 0.9950329 Reproduction
41 Digital cushion thickness 1 51 4461 163224 0.7567046 0.9951812 Exterior
164 Muscle potassium content 1 50 4461 163224 0.7498662 0.9951812 Meat and Carcass
216 White line disease 1 51 4461 163224 0.7567046 0.9951812 Exterior
3 Age at first calving 4 233 4461 163224 0.8823354 1.0000000 Reproduction
4 Age at puberty 5 8222 4461 163224 1.0000000 1.0000000 Reproduction
20 Bone weight 3 402 4461 163224 0.9989077 1.0000000 Meat and Carcass
21 Bovine coronavirus susceptibility 1 73 4461 163224 0.8677886 1.0000000 Health
23 Bovine tuberculosis susceptibility 14 1155 4461 163224 0.9998691 1.0000000 Health
25 Calf size 1 69 4461 163224 0.8522835 1.0000000 Reproduction
27 Calving ease 70 3819 4461 163224 0.9998937 1.0000000 Reproduction
35 Connective tissue amount 66 3142 4461 163224 0.9900416 1.0000000 Meat and Carcass
42 Digital dermatitis 2 110 4461 163224 0.8060144 1.0000000 Exterior
44 Dry matter intake 15 2186 4461 163224 1.0000000 1.0000000 Production
49 Fat thickness at the 12th rib 2 169 4461 163224 0.9469097 1.0000000 Meat and Carcass
58 Gestation length 10 636 4461 163224 0.9800620 1.0000000 Reproduction
61 Hoof and leg disorders 1 60 4461 163224 0.8104235 1.0000000 Exterior
63 Inhibin level 4 293 4461 163224 0.9599126 1.0000000 Reproduction
66 Insulin-like growth factor 1 level 2 129 4461 163224 0.8705023 1.0000000 Health
69 Interval from first to last insemination 6 445 4461 163224 0.9828601 1.0000000 Reproduction
70 Interval to first estrus after calving 20 1053 4461 163224 0.9666930 1.0000000 Reproduction
80 Longissimus muscle area 33 1420 4461 163224 0.8494685 1.0000000 Meat and Carcass
81 Luteal activity 14 1306 4461 163224 0.9999909 1.0000000 Reproduction
82 M. paratuberculosis susceptibility 11 492 4461 163224 0.7887653 1.0000000 Health
84 Marbling score 31 1817 4461 163224 0.9984368 1.0000000 Meat and Carcass
86 Maturity rate 2 243 4461 163224 0.9907192 1.0000000 Production
91 Milk alpha-S1-casein percentage 1 65 4461 163224 0.8349606 1.0000000 Milk
93 Milk arachidic acid content 1 60 4461 163224 0.8104235 1.0000000 Milk
94 Milk beta-lactoglobulin percentage 4 291 4461 163224 0.9583769 1.0000000 Milk
97 Milk C14 index 51 4437 4461 163224 1.0000000 1.0000000 Milk
98 Milk C16 index 36 2002 4461 163224 0.9974754 1.0000000 Milk
99 Milk C18 index 1 246 4461 163224 0.9989106 1.0000000 Milk
101 Milk caproic acid content 12 675 4461 163224 0.9575803 1.0000000 Milk
104 Milk casein percentage 4 259 4461 163224 0.9251845 1.0000000 Milk
107 Milk conjugated linoleic acid content 4 274 4461 163224 0.9429551 1.0000000 Milk
111 Milk fat percentage 138 10941 4461 163224 1.0000000 1.0000000 Milk
112 Milk fat yield 147 8220 4461 163224 1.0000000 1.0000000 Milk
115 Milk iron content 2 217 4461 163224 0.9826892 1.0000000 Milk
121 Milk linoleic acid content 5 857 4461 163224 0.9999992 1.0000000 Milk
127 Milk myristoleic acid content 38 3047 4461 163224 1.0000000 1.0000000 Milk
129 Milk palmitic acid content 12 1711 4461 163224 1.0000000 1.0000000 Milk
130 Milk palmitoleic acid content 22 2422 4461 163224 1.0000000 1.0000000 Milk
131 Milk pentadecylic acid content 6 280 4461 163224 0.7788727 1.0000000 Milk
133 Milk phosphorus content 2 154 4461 163224 0.9254224 1.0000000 Milk
143 Milk stearic acid content 3 218 4461 163224 0.9387352 1.0000000 Milk
144 Milk tetracosanoic acid content 2 105 4461 163224 0.7848243 1.0000000 Milk
147 Milk unglycosylated kappa-casein percentage 50 2351 4461 163224 0.9735648 1.0000000 Milk
150 Milk yield 165 6432 4461 163224 0.8101634 1.0000000 Milk
159 Muscle carnosine content 1 67 4461 163224 0.8438621 1.0000000 Meat and Carcass
166 Muscle total collagen content 1 68 4461 163224 0.8481312 1.0000000 Meat and Carcass
184 Rump angle 3 179 4461 163224 0.8696818 1.0000000 Exterior
188 Scrotal circumference 15 8064 4461 163224 1.0000000 1.0000000 Reproduction
190 Shear force 73 2954 4461 163224 0.8253604 1.0000000 Meat and Carcass
202 Subcutaneous fat thickness 3 331 4461 163224 0.9944835 1.0000000 Meat and Carcass
206 Tenderness score 45 3483 4461 163224 1.0000000 1.0000000 Meat and Carcass
207 Tick resistance 1 89 4461 163224 0.9151568 1.0000000 Health

My Image

Enrichment by QTL_type (enrichment processes performed for the QTL classes)

X QTL N_QTLs N_QTLs_db Total_annotated_QTLs Total_QTLs_db pvalue adj.pval
1 Exterior 304 9077 4461 163224 0.0001814 0.0003628
2 Health 265 5889 4461 163224 0.0000000 0.0000000
3 Meat and Carcass 503 18258 4461 163224 0.4308370 0.6462554
4 Milk 1996 75352 4461 163224 0.9742520 1.0000000
5 Production 814 19640 4461 163224 0.0000000 0.0000000
6 Reproduction 579 35008 4461 163224 1.0000000 1.0000000

My Image

9.5 Comparison among ssGWAS for independent SNPs against Window 0.5% and window 0.1%

9.5.1 SNPs

pvalue <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/gwas_ind_seg_sig_SNPname_rsID.csv")
w05 <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_05.csv")
w01 <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_01.csv")



intersecpt_pv_w05_name <- intersect(pvalue$SNP_name, w05$SNP_name)
intersecpt_pv_w01_name <- intersect(pvalue$SNP_name, w01$SNP_ID)


# Create the matrix
common_ids <- matrix(NA, nrow = length(intersecpt_pv_w01_name), ncol = 3)
common_ids[, 1] <- intersecpt_pv_w01_name  # First column
common_ids[, 2] <- ifelse(intersecpt_pv_w01_name %in% intersecpt_pv_w01_name, "YES", NA)
common_ids[, 3] <- ifelse(intersecpt_pv_w01_name %in% intersecpt_pv_w05_name, "YES", NA)
common_ids <- as.data.frame(common_ids)
colnames(common_ids) <- c("P_value_SNPs", "W_0.1%_SNPs", "W_0.5%_SNPs")
common_ids$rsID <- pvalue$rsID[match(common_ids$P_value_SNPs, pvalue$SNP_name)]
common_ids <- common_ids[, c("P_value_SNPs", "rsID", "W_0.1%_SNPs", "W_0.5%_SNPs")]

write_csv(common_ids, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/common_SNPs.csv")

Bellow are the SNPs which had the p-value bellow bonferroni and also apeeared in the set of SNPs which explain more thant 0.5% or 0.1% of genetic variance.

P_value_SNPs rsID W_0.1._SNPs W_0.5._SNPs
ARS-BFGL-NGS-25298 rs109868537 YES YES
ARS-BFGL-NGS-37809 rs42751504 YES YES
ARS-BFGL-NGS-74948 rs41585925 YES NA
ARS-BFGL-NGS-82859 rs110506037 YES NA
BTB-01641394 rs42752353 YES YES

9.5.2 Genes

pvalue <- read.csv("/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/out_genes_50k_pvalue.csv")
w05 <- read.csv("/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_genes_w_05.csv")
w01 <- read.csv("/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/windows10/out_genes_w01.csv")

intersecpt_pv_w05_name <- intersect(pvalue$gene_name, w05$gene_name)
intersecpt_pv_w05_ID <- intersect(pvalue$gene_id, w05$gene_id)

intersecpt_pv_w01_name <- intersect(pvalue$gene_name, w01$gene_name)
intersecpt_pv_w01_ID <- intersect(pvalue$gene_id, w01$gene_id)

# Create the matrix
common_ids <- matrix(NA, nrow = length(intersecpt_pv_w01_ID), ncol = 3)
common_ids[, 1] <- intersecpt_pv_w01_ID  # First column
common_ids[, 2] <- ifelse(intersecpt_pv_w01_ID %in% intersecpt_pv_w01_ID, "YES", NA)
common_ids[, 3] <- ifelse(intersecpt_pv_w01_ID %in% intersecpt_pv_w05_ID, "YES", NA)
common_ids <- as.data.frame(common_ids)
colnames(common_ids) <- c("P_value_id", "W_0.1%", "W_0.5%")
common_ids$P_value_name <- pvalue$gene_name[match(common_ids$P_value_id, pvalue$gene_id)]
common_ids <- common_ids[, c("P_value_id", "P_value_name", "W_0.1%", "W_0.5%")]
common_ids$gene_biotype <- pvalue$gene_biotype[match(common_ids$P_value_id, pvalue$gene_id)]

write_csv(common_ids, "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/common_genes.csv")
Bellow we can find the Genes that were found close (50kb) to SNPs that were:
  • Individually significant (P_value)
  • Explain more than 0.5% additive genetic variance
  • Explain more than 0.1% additive genetic variance
P_value_id P_value_name W_0.1. W_0.5. gene_biotype
ENSBTAG00000002950 TMEM132D YES NA protein_coding
ENSBTAG00000020798 C3H1orf94 YES NA protein_coding
ENSBTAG00000005784 CSMD2 YES YES protein_coding
ENSBTAG00000013537 FER1L6 YES NA protein_coding
ENSBTAG00000048373 NA YES NA protein_coding
ENSBTAG00000030718 SPATA3 YES NA protein_coding
ENSBTAG00000054626 NA YES NA lncRNA

9.5.3 QTLs

# GALLO

#import a QTL annotation file
qtl_UCD1_2 <- import_gff_gtf(db_file="/home/bambrozi/2_CORTISOL/GALLO/Animal_QTLdb_release53_cattleARS_UCD1.gff.gz",file_type="gff")


#import MARKER files = the GWAS output
gwas_p = read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/gwas_ind_seg_sig_SNPname_rsID.csv")
colnames(gwas_p) <- c("X", "SNP", "rsID", "CHR", "BP", "LOG_P")

pvalue <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                         marker_file= gwas_p, 
                                         method = "qtl",
                                         marker = "snp", 
                                         interval = 50000, 
                                         nThreads = NULL)

gwas_w05 <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_05.csv")
colnames(gwas_w05) <- c("X", "SNP", "rsID", "CHR", "BP", "Var")

w05 <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                         marker_file= gwas_w05, 
                                         method = "qtl",
                                         marker = "snp", 
                                         interval = 50000, 
                                         nThreads = NULL)

gwas_w01 <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w10_snp_rsid_snpvar_01.csv")
colnames(gwas_w01)
colnames(gwas_w01) <- c("X", "CHR", "BP", "SNP", "Var")
gwas_w01 <- gwas_w01[, c("X","SNP", "CHR", "BP", "Var")]

w01 <- find_genes_qtls_around_markers(db_file= qtl_UCD1_2, 
                                          marker_file= gwas_w01, 
                                          method = "qtl",
                                          marker = "snp", 
                                          interval = 50000, 
                                          nThreads = NULL)



#################################################################

intersecpt_pv_w05_ID <- intersect(pvalue$QTL_ID, w05$QTL_ID)

intersecpt_pv_w01_ID <- intersect(pvalue$QTL_ID, w01$QTL_ID)

# Create the matrix
common_ids <- matrix(NA, nrow = length(intersecpt_pv_w01_ID), ncol = 3)
common_ids[, 1] <- intersecpt_pv_w01_ID  # First column
common_ids[, 2] <- ifelse(intersecpt_pv_w01_ID %in% intersecpt_pv_w01_ID, "YES", NA)
common_ids[, 3] <- ifelse(intersecpt_pv_w01_ID %in% intersecpt_pv_w05_ID, "YES", NA)
common_ids <- as.data.frame(common_ids)
colnames(common_ids) <- c("P_value_id", "W_0.1%", "W_0.5%")
common_ids$P_value_name <- pvalue$Name[match(common_ids$P_value_id, pvalue$QTL_ID)]
common_ids <- common_ids[, c("P_value_id", "P_value_name", "W_0.1%", "W_0.5%")]
common_ids$QTL_type <- pvalue$QTL_type[match(common_ids$P_value_id, pvalue$QTL_ID)]

write_csv(common_ids, "/home/bambrozi/2_CORTISOL/GALLO/GWAS_BLUPF90/common_QTLs.csv")
Bellow we can find QTLs that were found close (50kb) to SNPs that were:
  • Individually significant (P_value)
  • Explain more than 0.5% additive genetic variance
  • Explain more than 0.1% additive genetic variance
P_value_id W_0.1. W_0.5. P_value_name QTL_type
137202 YES NA Bovine respiratory disease susceptibility Health
170274 YES NA Fecal larva count Health
170275 YES NA Fecal larva count Health
170276 YES NA Fecal larva count Health
30008 YES YES Interval to first estrus after calving Reproduction
30244 YES YES Interval to first estrus after calving Reproduction
152258 YES YES Muscle sodium content Meat_and_Carcass
225527 YES YES Shear force Meat_and_Carcass
25606 YES NA Milk yield Milk
26355 YES NA Milk protein yield Milk
36989 YES NA Lean meat yield Meat_and_Carcass
104606 YES NA Milk fat percentage Milk
104607 YES NA Milk fat percentage Milk
220989 YES NA Interdigital hyperplasia Exterior
122755 YES NA Length of productive life Production
123539 YES NA Length of productive life Production
122756 YES NA Length of productive life Production
123540 YES NA Length of productive life Production
122757 YES NA Length of productive life Production
123541 YES NA Length of productive life Production
122758 YES NA Length of productive life Production
123542 YES NA Length of productive life Production
122759 YES NA Length of productive life Production
123543 YES NA Length of productive life Production
122760 YES NA Length of productive life Production
123544 YES NA Length of productive life Production
200902 YES NA Milk pentadecylic acid content Milk
202500 YES NA Milk pentadecylic acid content Milk
202905 YES NA Milk pentadecylic acid content Milk
201638 YES NA Milk pentadecylic acid content Milk
202885 YES NA Milk pentadecylic acid content Milk
39599 YES NA Calving ease Reproduction
39600 YES NA Pregnancy rate Reproduction
39601 YES NA Stillbirth Reproduction
39602 YES NA Foot angle Exterior
39603 YES NA Feet and leg conformation Exterior
39604 YES NA Milk fat percentage Milk
39605 YES NA PTA type Production
39606 YES NA Udder attachment Exterior
39607 YES NA Milk fat yield Milk
39608 YES NA Net merit Production
39609 YES NA Length of productive life Production
39610 YES NA Rear leg placement - side view Exterior
39611 YES NA Udder height Exterior
39612 YES NA Rump width Production
39613 YES NA Calving ease Reproduction
39614 YES NA Somatic cell score Health
39615 YES NA Strength Exterior
39616 YES NA Udder depth Exterior
125235 YES NA Multiple birth Reproduction

9.6 Allele frequency for GWAS output

I created the code bellow to find out the allele frequency for those most significant SNPs considering the two different methodologies, the “regular”ssGWAS and the window ssGWAS.

# Bringing SNP_ID to SNP_Frequency
snpmap <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/snpmap.txt_clean", header=T)
snpfreq <- read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/freqdata.count")
map_freq_var <- cbind(snpmap, snpfreq,snpWvar)
colnames(map_freq_var) <- c("CHR", "POS", "SNP_ID", "SNP_ORDER", "FREQ", "V1", "V2", "Var", "snp", "chr", "pos")
map_freq_var <- map_freq_var[,c("CHR", "POS", "SNP_ORDER", "SNP_ID", "FREQ", "Var") ]
map_freq_var_w05 <- filter(map_freq_var, Var > 0.5)
map_freq_var_w01 <- filter(map_freq_var, Var > 0.1)
write.csv(map_freq_var_w05, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w05_allele_freq.csv")
write.csv(map_freq_var_w01, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/window_10/w01_allele_freq.csv")



# Output for significant p-value

#Estimating Bonferroni for genome independent segments
Genome_Assembly_ARS_UCD_1_2 <- read_tsv("/home/bambrozi/2_CORTISOL/GWAS/sequence_report_ARS-UCD1_2.tsv")

library(dplyr)
# Filter the rows and sum the Seq length column
# Assuming your data frame is named Genome_Assembly_ARS_UCD_1_2
L <- Genome_Assembly_ARS_UCD_1_2 %>%
  filter(`UCSC style name` %in% paste0("chr", 1:29)) %>%
  summarise(total_length = sum(`Seq length`)) %>%
  pull(total_length)
# Converting bases to Morgan (1Mb = 1cM (0,01 Morgan))
L_M <- L/10^8
# The Ne measure is based on the article bellow:
Ne <- 66 #(Makanjoula et al., 2020)
NeL <- Ne*L_M
# This is the number of independent segment in the genome.
Me <- (2*NeL)/log10(NeL)
# Calculate Bonferroni threshold (already done)
bonf <- -log10(0.05 / Me)

library(tidyverse)
#import MARKER files = the GWAS output
snpPval = read.table("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/chrsnp_pval")
colnames(snpPval) <- c("V1", "V2", "LOG_P", "SNP", "CHR", "BP")
map_freq_logp <- cbind(snpmap, snpfreq,snpPval)
map_freq_logp <- map_freq_logp[, c("CHR", "POS", "SNP", "SNP_ID", "V2", "LOG_P")]
colnames(map_freq_logp) <- c("CHR", "POS", "SNP", "SNP_ID", "FREQ", "LOG_P")
map_freq_logp <- filter(map_freq_logp, LOG_P >= bonf)
write.csv(map_freq_logp, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/pval_allele_freq.csv")


common_snp <- read.csv("/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/common_SNPs.csv")
common_snp$allele_freq <- map_freq_logp$FREQ[match(common_snp$P_value_SNPs, map_freq_logp$SNP_ID)]
write.csv(map_freq_logp, "/home/bambrozi/2_CORTISOL/GWAS/BLUPF90/EXTREME_PHENO/common_SNPs_FREQ.csv")

9.6.1 “Regular” ssGWAS

X CHR POS SNP SNP_ID FREQ LOG_P
1 2 41602429 3497 BTB-00096979 0.746479 4.497977
2 2 41670655 3498 Hapmap48777-BTA-47434 0.760563 4.377284
3 2 118820218 4632 Hapmap53065-rs29026778 0.535211 4.420962
4 3 110866523 6862 ARS-BFGL-NGS-118207 0.422535 4.815943
5 3 111526170 6873 ARS-BFGL-NGS-74948 0.535211 4.760351
6 3 111730561 6880 BTB-01641394 0.507042 4.390707
7 3 111751663 6881 ARS-BFGL-NGS-37809 0.507042 4.390707
8 3 111772736 6882 ARS-BFGL-NGS-25298 0.612676 5.088738
9 4 7873471 7184 Hapmap60503-rs29018741 0.211268 4.610767
10 4 24239851 7471 Hapmap60681-rs29013301 0.063380 4.641241
11 4 25933587 7502 Hapmap50554-BTA-107048 0.126761 4.352451
12 4 26511448 7515 Hapmap59011-rs29027498 0.415493 4.537662
13 4 27021431 7524 Hapmap59743-rs29017061 0.246479 5.045447
14 4 27094376 7526 BTB-00170785 0.119718 5.485063
15 4 50755462 7910 ARS-BFGL-NGS-12139 0.049296 5.015905
16 4 95650788 8704 Hapmap58854-rs29023486 0.091549 4.825103
17 4 103979600 8831 ARS-BFGL-NGS-110705 0.683099 5.591006
18 4 107787202 8888 ARS-BFGL-NGS-45265 0.661972 5.375628
19 4 109568557 8904 Hapmap48062-BTA-72409 0.345070 4.709882
20 4 110053134 8911 UA-IFASA-2147 0.542254 5.245598
21 6 36184467 11511 Hapmap23854-BTC-062412 0.232394 4.758110
22 7 110306791 14772 BTB-01148543 0.528169 4.609691
23 11 19779915 20558 Hapmap55558-rs29013980 0.352113 4.828190
24 12 17477322 22252 BTB-00488482 0.161972 4.417576
25 14 15929822 25167 ARS-BFGL-NGS-82859 0.598592 5.467439
26 15 33066384 26782 BTB-00594449 0.105634 5.041271
27 15 57930281 27199 ARS-BFGL-NGS-30515 0.154930 4.514377
28 17 46487068 29742 ARS-BFGL-NGS-12510 0.154930 4.547766
29 17 47884497 29772 ARS-BFGL-NGS-87412 0.753521 6.622354
30 17 48800954 29784 ARS-BFGL-NGS-112149 0.154930 5.268940
31 20 60365668 33553 BTB-01341053 0.732394 4.603817
32 20 65351653 33665 ARS-BFGL-NGS-91119 0.084507 4.891206
33 21 4055731 33836 ARS-BFGL-NGS-112210 0.922535 4.493938
34 23 44822126 36688 ARS-BFGL-NGS-115605 0.802817 4.316434
35 24 857728 36876 Hapmap47669-BTA-59022 0.669014 4.458018
36 28 20016672 40795 ARS-BFGL-NGS-116552 0.739437 5.130557
37 28 35807366 41057 ARS-BFGL-NGS-71077 0.514085 4.695875
38 29 33039863 41785 ARS-BFGL-NGS-41631 0.859155 4.933140

9.6.2 Windows ssGWAS

9.6.2.1 0.5%

X CHR POS SNP_ORDER SNP_ID FREQ Var
1 2 115622067 4582 Hapmap41888-BTA-49091 0.232394 0.8268392
2 2 115665427 4583 ARS-BFGL-BAC-35548 0.542254 1.2851806
3 2 115695003 4584 BTA-49096-no-rs 0.760563 1.6071883
4 2 115730530 4585 ARS-BFGL-NGS-30337 0.521127 1.3488469
5 2 115821065 4586 ARS-BFGL-NGS-103753 0.612676 1.1689686
6 2 115875702 4587 Hapmap54770-rs29009608 0.535211 0.7398417
7 2 115986085 4588 ARS-BFGL-NGS-43721 0.605634 0.8178265
8 2 116018639 4589 ARS-BFGL-NGS-107330 0.366197 0.5958551
9 3 111677167 6878 BTB-01948148 0.035211 0.7467347
10 3 111708236 6879 Hapmap42062-BTA-109789 0.260563 1.2415855
11 3 111730561 6880 BTB-01641394 0.507042 0.9480214
12 3 111751663 6881 ARS-BFGL-NGS-37809 0.507042 1.0607739
13 3 111772736 6882 ARS-BFGL-NGS-25298 0.612676 1.2101267
14 3 111806406 6883 ARS-BFGL-NGS-44131 0.176056 1.0252690
15 3 111833768 6884 ARS-BFGL-NGS-6202 0.302817 1.1671762
16 3 111933069 6885 ARS-BFGL-NGS-85333 0.211268 0.6077153
17 3 111965305 6886 ARS-BFGL-NGS-97849 0.373239 0.7547885
18 5 6312610 9246 BTA-73915-no-rs 0.267606 0.5103844
19 10 50554831 19228 BTB-01434227 0.591549 0.6660810
20 13 18266073 23616 Hapmap50266-BTA-13664 0.239437 0.6006650
21 13 18386942 23617 Hapmap49833-BTA-103929 0.823944 0.5282867
22 13 18509812 23619 BTA-25900-no-rs 0.732394 0.6364128
23 13 18558540 23620 ARS-BFGL-BAC-7444 0.619718 0.6630789
24 15 10879514 26382 BTB-01813405 0.253521 0.5556342
25 15 10906064 26383 BTB-02063964 0.253521 0.8788211
26 15 10936002 26384 BTB-01830390 0.352113 1.0480277
27 15 10974997 26385 BTB-01608944 0.845070 1.1206945
28 15 11100934 26386 BTA-91816-no-rs 0.267606 0.9739683
29 15 11144666 26387 BTB-01421844 0.732394 0.7131750
30 15 11185546 26388 BTB-01421892 0.732394 0.6463689
31 15 11207429 26389 BTB-01421934 0.711268 0.8898567
32 15 11236303 26390 BTB-01422008 0.711268 0.6832306
33 15 34054485 26800 ARS-BFGL-NGS-2713 0.816901 0.5549892
34 15 34109962 26801 ARS-BFGL-NGS-98724 0.598592 0.6252263
35 15 34144843 26802 ARS-BFGL-NGS-5976 0.478873 0.5490537
36 20 12087403 32778 ARS-BFGL-NGS-3276 0.352113 0.5722138
37 20 12111883 32779 ARS-BFGL-NGS-78615 0.295775 0.5689079
38 24 28417928 37341 ARS-BFGL-NGS-5141 0.204225 0.5945940
39 24 28487771 37343 ARS-BFGL-BAC-28665 0.873239 0.6899732
40 24 28516684 37344 Hapmap54981-rs29019846 0.816901 0.7747524
41 24 28540641 37345 BTB-01485274 0.591549 0.8983644
42 24 28570245 37346 Hapmap58887-rs29013502 0.647887 0.7619213
43 24 28604672 37347 BTB-01646599 0.591549 0.5894579

9.6.2.2 0.1%

As this table has 1,089 rows we are not showing here, but we are going to show bellow the allele frequency of the common SNPs among p-value, Windows 0.5% and Windows 0.1% approach:

X P_value_SNPs rsID W_0.1._SNPs W_0.5._SNPs allele_freq
1 ARS-BFGL-NGS-25298 rs109868537 YES YES 0.612676
2 ARS-BFGL-NGS-37809 rs42751504 YES YES 0.507042
3 ARS-BFGL-NGS-74948 rs41585925 YES NA 0.535211
4 ARS-BFGL-NGS-82859 rs110506037 YES NA 0.598592
5 BTB-01641394 rs42752353 YES YES 0.507042

10 Genetic Correlation

To assess the correlation between Cortisol phenotypes and Genomic Estimated Breeding Values (GEBVs), we opt for a linear regression instead of a standard correlation test. This decision is driven by the non-normal distribution of our Cortisol phenotypes, which violates the assumptions required for traditional correlation tests.

Linear regression offers a robust alternative as it does not necessitate normality for the dependent variable. By regressing GEBVs over Cortisol, we can model the relationship between these variables. Our aim is to estimate the regression coefficient, which serves as our correlation estimate.

Due to the violation of normality assumptions for the dependent variable (Cortisol), traditional correlation tests may not provide reliable results, particularly in assessing the significance of the correlation. Therefore, alternative approaches, such as linear regression, are preferred as they do not require the same assumptions about the distribution of the dependent variable. By using linear regression, we can still assess the relationship between Cortisol and GEBVs while accommodating the non-normality of Cortisol phenotypes.

The regression model can be represented as follows: \[ y = \beta_0 + \beta_1 \times GEBV_{\text{Milk}} + \epsilon \]

Where:

This approach enables us to quantify the relationship between Cortisol and GEBVs, addressing the non-normality of Cortisol phenotypes while allowing for formal hypothesis testing of the correlation’s significance.

10.1 Data preparation

The first data I received from Lucas had only 135 animals out of 260 with values the other 125 had only NA I shown this to Lucas Lucas wrote to Alisson Lucas sent me the missing animals I merged this two files

rm(list = ls())

# Load the necessary library
library(dplyr)
library(tidyverse)

cortisol_260 <- read.csv("/home/bambrozi/2_CORTISOL/Data/data_clean.csv")

#This is the first dataframe with information for 135 animals and 125 NA
GEBVs1 <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebvs_elora.csv")
#This is the second file with information for the 125 NA animals
GEBVs2 <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/elora_missing_females_2404_06_11_2024.csv")
#This are de columns we can use because we know the meaning of the acronyms
GEBVs_to_use <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebv_names_lucas_06102024_BAG.csv")


sum(is.na(GEBVs1$MILK))
GEBVs1<- GEBVs1[which(is.na(GEBVs1[,"DHI_BARN_NAME"]) == F),]

sum(!is.na(GEBVs2$MILK))
GEBVs2<- GEBVs2[which(is.na(GEBVs2[,"DHI_BARN_NAME"]) == F),]

print(GEBVs1$DHI_BARN_NAME)
print(GEBVs2$DHI_BARN_NAME)

# Making the two dataframes with the same columns
# Remove elora_id and international_id from GEBVs1
GEBVs1 <- GEBVs1 %>% select(-elora_id, -international_id)

# Remove ANIMAL_ID from GEBVs2
GEBVs2 <- GEBVs2 %>% select(-ANIMAL_ID)

# Check if the two dataframes have the same columns
have_same_columns <- all(names(GEBVs1) == names(GEBVs2))

if (have_same_columns) {
  print("The dataframes have the same columns.")
} else {
  print("The dataframes do not have the same columns.")
}


# Check if the column names are in the same order
same_order <- identical(names(GEBVs1), names(GEBVs2))

if (same_order) {
  print("The columns are in the same order.")
} else {
  print("The columns are not in the same order.")
}

GEBVs_combined <- rbind(GEBVs1, GEBVs2)

# Sort the columns
sorted_cortisol_260 <- sort(cortisol_260$ID)
sorted_GEBVs_combined <- sort(GEBVs_combined$DHI_BARN_NAME)

# Check if the sorted columns have the same values
identical(sorted_cortisol_260, sorted_GEBVs_combined)

# Create a duplicate of the column 'DHI_BARN_NAME' and name it 'elora_id'
GEBVs_combined$elora_id <- GEBVs_combined$DHI_BARN_NAME

# Assuming GEBVs_combined is your data frame
GEBVs_combined <- GEBVs_combined %>%
  select(elora_id, DHI_BARN_NAME, everything())

write.csv(GEBVs_combined, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/ebvs_elora_complete.csv")

# Merging the dataframe with Cortisol values, with the dataframe with GEBVs values
Merg_Cort_GEBVs <- merge(cortisol_260, GEBVs_combined, by.x = "ID", by.y = "elora_id")

write.csv(Merg_Cort_GEBVs, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/Merged_Cortisol_GEBVs.csv")

#Opening the file with the GEBVs columns to use
Columns_to_use <- readLines("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/traits_to_use.txt")

colnames(Merg_Cort_GEBVs)[405] <- "IDD"

data <- select(Merg_Cort_GEBVs, ID, T4Cortisol, BIRTH_YEAR, all_of(Columns_to_use))

# The data below has the the 55 GEBVs + Cortisol data + Birth Year
write.csv(data, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits.csv")

samp_date2 <- read.csv("/home/bambrozi/2_CORTISOL/Data/Elora animal_ids_kl_sampling_date.csv")

# Convert Sampling_date to Date using as.Date
samp_date$Sampling_date <- as.Date(samp_date$Sampling_date, format = "%m/%d/%Y")

table(samp_date$Sampling_date)

samp_date <- select(samp_date, Elora_id, Sampling_date)

# Check if data$ID and samp_dates$elora_id are identical in values and order
identical(data$ID, samp_date$Elora_id)

data_final <- merge(data, samp_date, by.x="ID", by.y="Elora_id")

data_final <- data_final %>%
  select(ID, T4Cortisol, BIRTH_YEAR, Sampling_date, everything())

# The data below has the the 55 GEBVs + Cortisol data + Birth Year + Sampling data
write.csv(data_final, "/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits2.csv")

ps. I double checked by hand the select and merge process against the original tables received and is everything ok.

10.2 Correlations - Linear Regression

We tested the minimum model presented above, and adding only Birth Year, but adding Birth Year and Sampling Date we got the best results.

10.2.1 Adding BIRTH_YEAR and SAMPLING DATE

The regression model added the BY and SAMPLING DATE is shown bellow:

\[ y = \beta_0 + \beta_1 \times GEBV_{\text{Trait}} + BIRTH\_YEAR + SAMPLING\_DATE + \epsilon \]

Where:

  • \(y\) represents Cortisol phenotypes.
  • \(GEBV_{\text{Trait}}\) denotes the GEBV for the specific trait (e.g., Milk Yield).
  • \(BIRTH\_YEAR\) is the birth year of the subjects, included as a factor.
  • \(SAMPLING\_DATE\) is the cortisol sampling date for the subjects, included as a factor.
  • \(\beta_0\) and \(\beta_1\) are the intercept and regression coefficient, respectively.
  • \(\epsilon\) represents the error term capturing unexplained variability.

The SAMPLING_DATE variable is also converted to a factor to account for the categorical nature of sampling date.

# Convert BIRTH_YEAR to a factor and rename
data_final$BIRTH_YEAR <- as.factor(data_final$BIRTH_YEAR)

# Convert Sampling_data to a factor and rename
data_final$Sampling_date <- as.factor(data_final$Sampling_date)

# Initialize a list to store the results
results_list <- list()

# Loop through columns 3 to ncol(data) for the GEBVs
for (i in 5:ncol(data_final)) {
  trait_name <- colnames(data_final)[i]
  
  # Fit the linear regression model with BIRTH_YEAR as an additional predictor
  model <- lm(data_final[[2]] ~ data_final[[i]] + data_final$BIRTH_YEAR + data_final$Sampling_date , data = data_final)
  
  # Summarize the model
  model_summary <- summary(model)
  
  # Extract the desired statistics
  multiple_r_squared <- model_summary$r.squared
  adjusted_r_squared <- model_summary$adj.r.squared
  f_statistic <- model_summary$fstatistic[1] # F-statistic value
  f_num_df <- model_summary$fstatistic[2] # Numerator degrees of freedom
  f_den_df <- model_summary$fstatistic[3] # Denominator degrees of freedom
  p_value <- pf(f_statistic, f_num_df, f_den_df, lower.tail = FALSE) # P-value
  
  # Extract the coefficient and its p-value for the trait
  coef_summary <- coef(model_summary)
  trait_coef <- coef_summary[2, "Estimate"]  # Assumes the trait is the second predictor
  trait_p_value <- coef_summary[2, "Pr(>|t|)"]
  
  # Combine the statistics into a data frame
  result <- data.frame(
    Trait = trait_name,
    Multiple_R_Squared = multiple_r_squared,
    Adjusted_R_Squared = adjusted_r_squared,
    F_Statistic = f_statistic,
    P_Value = p_value,
    Coefficient = trait_coef,
    Coefficient_P_Value = trait_p_value
  )
  
  # Append the result to the results list
  results_list[[i - 2]] <- result
}

# Combine all results into a single data frame
results_df <- do.call(rbind, results_list)

# Save the results to a CSV file
write.csv(results_df, file = "/home/bambrozi/2_CORTISOL/Correlation/Results/add_BY_SAMP/regression_summary_all_traits_BY_SampDt.csv", row.names = FALSE)

Summary statistics for all Traits’ GEBVs adding BIRTY_YEAR and SAMPLING_DATE

Trait Multiple_R_Squared Adjusted_R_Squared F_Statistic P_Value Coefficient Coefficient_P_Value
CO 0.3913617 0.3257757 5.967151 0 -11.1849879 0.0099861
BMR 0.3899307 0.3241904 5.931386 0 14.4612444 0.0135665
LP 0.3858330 0.3196512 5.829896 0 12.2619950 0.0330355
MILK 0.3850052 0.3187343 5.809559 0 0.0671160 0.0396652
PROT 0.3841834 0.3178238 5.789422 0 2.2166395 0.0476301
UT 0.3822859 0.3157218 5.743130 0 -12.1238483 0.0731558
CK 0.3801906 0.3134008 5.692345 0 12.7333709 0.1192726
HHE 0.3795579 0.3126999 5.677076 0 7.4003505 0.1388525
MSP 0.3793006 0.3124149 5.670876 0 -7.2388952 0.1478152
DA 0.3791391 0.3122360 5.666989 0 10.8037510 0.1537699
TU 0.3785722 0.3116080 5.653351 0 -5.2064639 0.1769405
MSL 0.3777098 0.3106526 5.632656 0 -8.6230634 0.2203509
BQ 0.3775224 0.3104450 5.628166 0 -7.7284891 0.2313766
IH 0.3772456 0.3101385 5.621542 0 -4.7354916 0.2488963
ST 0.3767980 0.3096426 5.610839 0 -9.9721644 0.2808121
LOC 0.3766890 0.3095218 5.608233 0 -7.2367609 0.2893509
FAT 0.3765584 0.3093772 5.605116 0 0.8520581 0.3000062
UD 0.3763515 0.3091480 5.600177 0 -9.3297651 0.3179533
CA 0.3757417 0.3084725 5.585641 0 6.0756898 0.3798799
MS 0.3755038 0.3082090 5.579979 0 -6.0375749 0.4085912
SCK 0.3754363 0.3081342 5.578372 0 -4.4161473 0.4173165
SCS 0.3754033 0.3080976 5.577587 0 3.9080681 0.4216769
IDD 0.3752677 0.3079474 5.574362 0 3.7029601 0.4403519
FOOT 0.3751830 0.3078536 5.572349 0 17.3309360 0.4526548
TL 0.3750803 0.3077398 5.569908 0 -6.6585204 0.4683140
MET 0.3749055 0.3075462 5.565755 0 -3.4014191 0.4970656
CTFS 0.3747600 0.3073850 5.562300 0 4.0557716 0.5233387
CONF 0.3746137 0.3072229 5.558828 0 -4.7896643 0.5523352
FE 0.3745078 0.3071056 5.556316 0 -2.9375586 0.5752627
HL 0.3743988 0.3069849 5.553731 0 3.4274416 0.6009102
FA 0.3742870 0.3068610 5.551080 0 -3.2821641 0.6298652
DD 0.3742836 0.3068573 5.551001 0 2.5402141 0.6307730
MOB 0.3742556 0.3068263 5.550337 0 3.0592146 0.6385442
FTP 0.3742359 0.3068045 5.549870 0 4.8583743 0.6441419
BCS 0.3741681 0.3067293 5.548263 0 2.6692352 0.6643615
HH 0.3740753 0.3066266 5.546066 0 2.0230249 0.6947785
DF 0.3740392 0.3065865 5.545209 0 2.3681806 0.7076996
FL 0.3739824 0.3065237 5.543865 0 -2.1711035 0.7294613
UF 0.3739605 0.3064994 5.543347 0 2.8880683 0.7384413
MDR 0.3738855 0.3064163 5.541571 0 1.8495373 0.7722558
WL 0.3738548 0.3063822 5.540843 0 1.2643028 0.7878801
AFS 0.3738293 0.3063540 5.540239 0 1.5814380 0.8018670
RUM 0.3738023 0.3063241 5.539601 0 -1.2574561 0.8179113
SH 0.3738015 0.3063233 5.539583 0 1.0342843 0.8184040
BD 0.3736958 0.3062061 5.537080 0 0.7613199 0.9071017
MR 0.3736957 0.3062060 5.537078 0 0.6291752 0.9071898
SU 0.3736871 0.3061965 5.536876 0 0.4745538 0.9186634
FEED 0.3736818 0.3061907 5.536751 0 0.3944746 0.9266753
DHL 0.3736774 0.3061858 5.536647 0 -0.5919776 0.9340753
DO 0.3736757 0.3061838 5.536604 0 0.5220723 0.9373261
DS 0.3736695 0.3061769 5.536458 0 0.3979132 0.9502668
CW 0.3736676 0.3061749 5.536414 0 0.3562368 0.9547808
ME 0.3736658 0.3061729 5.536370 0 -0.2401976 0.9599012
MT 0.3736595 0.3061659 5.536223 0 -0.0976386 0.9882642
DCA 0.3736593 0.3061657 5.536217 0 0.0802555 0.9906432
Fitting Birth_Year and Sampling_date to the model these are the traits with significant correlation (<0.15):
  • CO = Cystic ovaries
  • BMR = Body Maintenance Requirements
  • LP = Lactation persistency
  • MILK = Milk yield
  • PROT = Protein yield
  • UT = Udder Texture
  • CK = Clinical Ketosis
  • HHE = Heel Horn Erosion
  • MSP = Milking Speed

11 Heritability estimation - BLUPF90

11.1 Files preparation

Preparing files to run Variance components estimation using REML with AI (Average Information) algorithm.

First you need to create a directory in your home directory, prepare and save the following files in:

  • Phenotype and Fixed effects file
  • Pedigree file
  • Genotype file
  • BlupF90+ executable file
  • RenumF90 executable file
  • Parameter file

      11.1.1 Phenotype and Fixed effects file

      The appearance of this file is like this:

      My Image

      FIRST COLUMN = Animal ID SECOND COLUMN = Phenotype THIRD COLUMN = Fixed Effect 1 FOURTH COLUMN = Fixed Effect 2

      To get in one file these four columns we need the following code:

      #File with equivalence among different ids
      eq_ids <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/Pedigree/bruno_ids.csv")
      
      # Genotype file with cid
      geno <- read.table("/home/bambrozi/2_CORTISOL/Geno_files/genoplink.ped")
      
      # Phenotipic file and fixed effects
      data_final <- read.csv("/home/bambrozi/2_CORTISOL/RawFiles/GEBVs_Elora/data_GEBVs_Cortisol_select_traits2.csv")
      
      # creating a pheno file with only ID, Cortisol, BY and Sam date columns
      pheno <- data_final %>%
        select(ID, T4Cortisol, BIRTH_YEAR, Sampling_date)
      
      # Create a new column iid and and bring the iid from eq_ids to geno file
      pheno$iid <- eq_ids$iid[match(pheno$ID, eq_ids$elora_id)]
      
      # organizing columns sequence and keep only iid
      pheno <- pheno%>%
        select(iid, T4Cortisol, BIRTH_YEAR, Sampling_date)
      
      # Create a new column geno$iid, and bring the iid from eq_ids to geno file
      geno$iid <- eq_ids$iid[match(geno$V2, eq_ids$cdn_id)]
      
      # organizing the columns sequence
      library(dplyr)
      geno <- geno %>%
        select(V1, V2, iid, everything())
      
      # Keeping in the pheno file only the rows present also in geno file
      pheno <- pheno %>%
        filter(iid %in% geno$iid)
      
      write.table(pheno, "/home/bambrozi/2_CORTISOL/Heritability_BLUPF90/pheno_fix_eff.txt", sep = " ", col.names = FALSE, row.names = FALSE, quote = FALSE)

      The file should be saved as text file, with separation by space and no columns names.

      11.1.2 Pedigree file

      The appearance of this file is like this:

      My Image

      FIRST COLUMN = Animal ID SECOND COLUMN = Sire ID THIRD COLUMN = Dam ID

      The file should be saved as text file, with separation by space and no columns names.

      We used the code below to remove the commas of a .csv file to a file with sepation by spaces.

      # to replace comma for space in the .csv file with the equivalence among IDs
      sed -i 's/,/ /g' bruno_ids.csv

      11.1.3 Genotype file

      The appearance of this file is like this:

      My Image

      FIRST COLUMN = Animal ID SECOND COLUMN = Genotypes (0, 1 and 2 format)

      The file should be saved as text file, with separation by space and no columns names.

      We used the code below to replace the cid for iid. First we merge using the second column of the firs file, and the first column of the second file. Then we use again the command awk to keep only the third and fifth columsn and sabe in a different object.

      # Using the awk function to merge the two files and the second awk to select only the 3rd and 5fh columns
      awk 'FNR==NR {a[$2]=$0; next} {print a[$1], $0}' bruno_ids.csv bruno_gntps.txt | awk '{print$3,$5}' > bruno_gntps_iid

      11.1.4 Download the executable files

      Download from this website https://nce.ads.uga.edu/html/projects/programs/Linux/64bit/:
      • BlupF90+
      • renumF90

      11.1.5 Parameter file

      The appearance of this file is like this:

      My Image

      • DATAFILE: bellow this line you need to inform the name of the file with phenotype and fixed effects. As before running BLUPF90 on server you are going to direct the terminal to the directory where all these files are placed you only need to inform the name.
      • TRAITS: below this line you need to inform which column are the phenotype date in the previous file, in this example, 2.
      • FIELDS_PASSED TO OUTPUT:
      • WEIGHT (S):
      • RESIDUAL VARIANCE: for the firs run you need to inform the value of 1.0, for the second you can pick the variance from the firs run’s output.
      • EFFECT: you will inform your first effect, in this example, Birth Year, which is in the column 3, and the word cross numer because is a number.
      • EFFECT: you should provide the next effect, in this example, sample date, as sample date has one non numeric character you should inform as cross alpha, in this example column 4.
      • EFFECT: now I’m providing my animal ID information, in this example column 1, and again cross alpha because has number and letters in the ID. I’m also informing that this effect is RANDOM, and that is my animal effect.
      • FILE: bellow this line I need to provide the pedigree file. Again, as I’m already in the directory which contain the pedigree file I only need to provide the file name.
      • FILE-POS: Here I’ll inform which columns should be considered in the pedigree file, in this situation, 1 2 3 0 0.
      • PED_DEPTH: Now we can inform the depth we want the software considers the pedigree, or if we leave 0 it will the maximum possible.
      • (CO) VARIANCES: Here you should provide the Variance/Co-variance matrix, like as for residual variance in the first run we set up to 0 in this example that we don´t have to imagine any co-variance, but if you know that exist variance among you effects you shoul set up XXX for ….
      • OPTION method: VCE (Variance Component Estimation).
      • OPTION OrigID: this will keep the original ID informed.
      • OPTION missing 9999: you are informing that missing values will appear as 9999
      • OPTION se_covar_function: H2_1 g_3_3_1_1/(g_3_3_1_1+r_1_1)
        • H2_1: the name that your function will appear on the output files.
        • g_3_3: you are asking for genetic variance estimation for the 3rd informed effect.
        • **_1_1**: this effect is in the 1st column.
        • /(g_3_3_1_1+r_1_1): to get the total phenotipic variance, you are summing to genetic variance the residual variance of the effect in column 1.

11.2 Running renumF90 and BlupF90+

  1. Go to the server you wanna run this analysis, for instance, grand

  2. Now go to the directory you have created to run this analysis where that set of files are placed.

ssh grand
  1. Make the renumF90 and BlupF90+ files executables
chmod +x renumf90
chmod +x blupf90+
  1. Run renumF90
./renumf90

When you run the code above, it will as you the name of your parameter card.

renumF90 will generate a new parameter card called renf90.par

  1. Run blupf90+
./blupf90+

blupf90+ will ask you for parameter’s card name, now you should provide with the new one renf90.par

blupF90+ will generate the blupf90.log file with the results.

My Image

Now you should update you renf90.par file with these informations from the .log file

Copy Residual Variance from blupf90.log and will paste on renf90.par RANDOM_RESIDUAL_VALUES Copy Genetic variance for effect x from blupf90.log and will paste on renf90.par (CO) VARIANCE

  1. 2nd blupf90+ run
./blupf90+

blupf90+ will ask you for parameter’s card name, now you should provide with the UPDATED renf90.par

If the Residual Variance and Genetic variance for effect x didn’t change in your blupf90.log the analysis ended, but if this value vary, you should update again the renf90.par and run again blupf90+ until this values don’t change more.

11.3 Running renumF90 and BlupF90+ adding GENOTYPES

The previous analysis considered only the pedigree, but now we can insert the genotype information. To perform this you need a new diretory called Blup_Genomic inside your previously created directory.

Now you need add the reference for your genotype file in your previous parameter file renum.par and save in this new sub-directory.

The highlighted text show the added part.

My Image

Go to the sub-diretory

Note that as you are in the subdirectory, but your phenotype and fixed effect, pedigree and genotype files are still in the previous directory you need to add the highlighted part to inform the correct location

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To run the renumf90 and blupf90+ you also need to add ../ to correct specify the location. Run renumF90

./../renumf90

Run blupf90+

./../blupf90+

The steps for run, update parameter card, re-run are the same.

11.4 Results

We have 2 different output files

  1. Variance components: blupf90.log

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In this file we can find the heritabilit (SD) and for instance the convergence (similarity)

  1. Solutions: solutions.orig
    In this file we will find the solutions (results) for each effect

In our example:

  • EFFECT 1: Birth Year, has 4 levels (2018, 2019, 2020 and 2021), and the solution that for this fixed effect is how much each level add.
  • EFFECT 2: Sampling date, has 23 levels, and the solutions
  • EFFECT 3: Animal random effect, has one for each animal and it is the EBV or GEBV.

    My Image